text:
\section{Introduction}
Next-generation wireless networks are envisaged to support high-speed, low-latency and high-reliability communications, anywhere and anytime. Yet, more than 70 percent of the Earth surface is covered by oceans and the ever increasing activities scattered across the ocean have created great demand for maritime communications. At the time of writing, shipping mainly relies on satellites for seamless coverage~\cite{wei2021hybrid}. However, due to the wide coverage area of a satellite, the transmission bandwidth allocated to each user device is rather limited, even when high-throughput satellites are relied upon~\cite{vondra2018integration}. Moreover, geosynchronous-equatorial-orbit (GEO) satellites have a high propagation delay of about 120 ms, while their low-Earth-orbit (LEO) counterparts appear above the horizon for short duration and suffer from Doppler-effects.

On the other hand, the number of intercontinental passenger airplanes above the ocean is  significant and there is  an increasing demand for in-flight Internet connectivity. Similar to ships, airplanes also face the same satellite connection limitations as their maritime counterparts. In this context, the compelling concept of aeronautical ad-hoc networking (AANET) was proposed to form a self-configured wireless network via multihop air-to-air (A2A) communication links~\cite{zhang2019aeronautical}. Nevertheless, the coverage of AANETs hinge on the flight density, which fluctuates during a day.

Therefore, it is a nature inspiration to conceive the combination of satellites and airplanes to form a space-air-ground integrated network (SAGIN)~\cite{huang2019airplane,vondra2018integration} for supporting future maritime communications.
However, due to the inherent characteristics of heterogeneity, self-organization, and time-variability, the design and optimization of SAGINs faces numerous challenges~\cite{liu2018space}. A fundamental one is to design an efficient routing protocol for constructing an appropriate packet routing path at any given time in order to accommodate the high-dynamic network topology.

Recent advances in artificial intelligence have inspired diverse applications in wireless communications~\cite{luong2019applications,liu2020optimizing}, including maritime communications~\cite{yang2020ai}. Taking the routing problem for example, a deep learning (DL) aided routing algorithm was proposed in~\cite{kato2019optimizing} for balancing the traffic in SAGIN. Yet, the network topology was assumed to be static and the global node status had to be known for making routing decisions. To handle the high-dynamic network topology, a deep reinforcement learning (DRL) aided routing algorithm was conceived in~\cite{liu2021deep} for AANETs. By solely relying on local- rather than global-information, the powerful DRL-aided routing advocated is capable of achieving near-optimal end-to-end (E2E) delay.

Nevertheless, apart from the E2E delay, the overall network performance of SAGIN should be characterized by multiple metrics~\cite{jingjing}, such as the throughput and the path-lifetime to minimize rerouting for maintaining seamless connectivity. The heterogeneous services of SAGINs have different quality of service requirements and it is vital but challenging to strike compelling tradeoffs among the various potentially conflicting optimization objectives. Therefore, instead of finding a particular optimal solution such as the minimum-delay or maximum-throughput route, the ultimate goal is to discover the entire \emph{Pareto front} of all Pareto-optimal routes. Explicitly, for a Pareto-optimal delay-throughput pair, neither of them can be improved without sacrificing the other.

In this article, we propose a satellite-airplane-terrestrial  integrated solution for supporting ubiquitous high-quality maritime communications. In contrast to the satellite-UAV-terrestrial solution proposed in~\cite{li2020enabling}, where the air-layer is only beneficial for local coverage enhancement due to limited energy onboard of unmanned aerial vehicles (UAVs), our air-layer relies on the commercial passenger flights regularly flying across the ocean, hence providing long-duration and wide-range coverage. To solve the challenging multi-component routing-optimization problem high-dynamic networks, we design a DL-aided solution solely relying local- rather than global-information. Simulations are conducted based on real satellite, flight, and shipping data over the North Atlantic ocean. The results show the near-Pareto-optimality of our DL-aided multi-objective routing in terms of reducing  the E2E delay, increasing the E2E throughput, and enhancing the path-lifetime.
\section{Satellite-Airplane-Terrestrial Integrated Architecture}
In this section, we introduce the  multi-layer network architecture conceived for supporting maritime communications, compare the characteristics of different layers, and formulate the performance metrics.
\subsection{Multi-Layer Network Architecture}
The SAGIN is composed by three layers, namely the ground/sea-layer, the air-layer, and the space-layer, as shown in Fig.~\ref{fig:layer}.
\begin{figure}[!htb]
	\centering
	\includegraphics[width=0.47\textwidth]{layer}
	\caption{Multi-Layer Network Architecture}
	\label{fig:layer}
\end{figure}

\subsubsection{Ground/Sea-Layer}
The ground/sea-layer includes ships, on-shore base stations (BSs), and satellite/airplane ground station (GS). Each ship seeks a wireless connection to the BS or the GS on land. The on-shore BSs are deployed along the coast, but their coverage range is quite limited, connecting the ships that are densely clustered in the vicinity of ports, harbors, and waterways. The GSs are deployed for connecting the air- as well as space-layer and serve as the gateway for accessing the terrestrial core network.

\subsubsection{Air-Layer}
The air-layer is formed by the AANET consisting of commercial passenger airplanes. Each airplane is able to establish air-to-ground/sea (A2G) links with GSs or ships, as well as establish A2A links with nearby airplanes, when the transmitter and receiver are within the communication range. The A2G links operate at 14 GHz in the Ku-band with a bandwidth of 250 MHz according to Qualcomm's  Next-Gen Air-Ground system envisaged for in-flight broadband access. For implementation simplicity, we assume that the A2A links also operates at 14 GHz as in~\cite{vondra2018integration}.

The maximum A2G and A2A link distances mainly depend on the altitude of the transmitter and receiver, because the direct visibility of two points is limited by the curvature of the Earth. For example, given a common flight altitude of 12 km, the maximum A2G link and A2A link distances are about 390 km and 780 km, respectively. The maximum A2G link distance determines the maximal coverage area on the ground/sea, i.e. the footprint, of a single airplane. For the aforementioned example, the radius of an airplane's maximum footprint can be also estimated as 390 km. Given the velocity of airplanes, such as 900 km/h (i.e., 0.25 km/s) during cruise, the maximum visibility duration of an airplane observed at ground/sea (i.e. the maximum lifetime of an A2G link) can be estimated as 780/0.25 s $\approx$ 3120 s $=$ 52 min.
On the other hand, based on the maximum A2A link distance, it can be estimated that each airplane can be connected to dozens of other airplanes on average, given a practical flight density of $10^{-5}$ airplane/km$^2$~\cite{zhang2019aeronautical}, which makes multi-hop transmission possible for crossing the ocean.

\subsubsection{Space-Layer}
A LEO satellite constellation (SC) is considered for the space-layer due to its relatively low propagation-delay compared to GEO satellites. As an illustration, the LEO SC is deployed according to the Iridium-Next SC, which contains 66 operational LEO satellites at an altitude of 781 km.

To support high throughput, we consider satellites using Ka-band according to the 3GPP specifications, where both the downlink (at 20 GHz) and uplink (at 30 GHz), have a bandwidth of 400 MHz~\cite{3gpp2018study}.
Each satellite can communicate with its neighboring satellites via inter-satellite links at 23~GHz, and can form 48 spot-beams down to the Earth with a frequency reuse factor of three~\cite{vondra2018integration}. As a result, the footprint radius of each spot-beam is about 200 km and of the full beam is about 2300~km. The orbital velocity of a satellite can be calculated as 7.5 km/s according to its altitude, resulting in the maximum lifetime for a satellite-to-ground/sea (S2G) link of about 10 min, which is much lower than that of the AANET.

Although the total bandwidth of LEO SC is higher than that of the A2G link, it is shared by all the air and ground/sea devices within each spot-beam. By contrast,  multiple airplanes may be available for serving the devices within a certain area. Given a practical airplane density of $10^{-5}$ airplane/km$^2$, the available bandwidth per A2G link can be twice higher than that of per S2G link.

\subsection{Routing Performance Metrics}
To support various services, multiple  metrics should be considered for characterizing the routing performance in the SAGIN. In this article, we focus on the E2E delay, E2E throughput, and path-lifetime, which are defined as follows:

\begin{enumerate}
	\item The E2E delay is defined as the summation of all the delay components along the path from the source-node (SN) to destination-node (DN). Specifically, $D_{\sf link}(i,j)$ denotes the link-delay between nodes $i$ and $j$, which is composed of the propagation-delay and transmission-delay, and  $D_{\sf que}(j)$ denotes the queuing-delay at node $j$.
	\item The E2E throughput is defined as the achievable data-rate between the SN and DN, which is limited by the lowest-throughput link along the path, where $C(i,j)$ is the throughput between nodes $i$ and $j$, which can be calculated using Shannon's capacity formula.
	\item The path-lifetime is defined as the duration when every transmitter-receiver pair along the path has direct visibility, which is limited by the lowest link-lifetime along the path. We use $L(i,j)$ to denote the link-lifetime between nodes $i$ and $j$, which describes the duration of direct visibility between nodes $i$ and $j$.
\end{enumerate}

In the SAGIN considered, the maximum A2A link distance is shorter than the minimum S2G link. Therefore, the propagation-delay of each link in AAENT is lower than that in the LEO SC. However, since the AANET normally requires multiple hops for covering a long distance, say in the transatlantic scenario, it may result in longer accumulated queuing-delay, and hence increases the overall E2E delay.

On the other hand, due to the shorter link distance of AANETs and owing to their potentially higher available bandwidth per user device, the E2E throughput of AANET may be higher than that of LEO SC. Moreover, again, the velocity of an airplane is much lower than that of a LEO satellite, hence its path-lifetime is potentially longer than that of LEO SC, which may improve its routing stability. Nevertheless, the airplane density fluctuates during a day due to the flight schedules. Consequently, the coverage of AANET is time-varying. By contrast, LEO SC is capable of providing 24-hour global coverage. Therefore, it is worth investigating the integration of AANET and LEO SC for combining the benefits of both networks.

\section{Deep Learning Aided Routing}
For ease of exposition, we commence from a single-objective routing problem minimizing the E2E delay, and introduce the corresponding DL-aided routing algorithm. Then, we extend it to a challenging multi-objective scenario where the E2E delay, E2E throughput, and path-lifetime are simultaneously optimized.
\subsection{Single-Objective Routing}
When the global information regarding the network topology, the queuing-delay of each node and the link-delay between every two nodes are available, the E2E delay minimization problem can be solved by classic shortest-path search algorithms by treating the summation of link-delay and queuing-delay as the ``distance", i.e. the edge weight. However, the node positions change rapidly due to the high velocity of airplanes and LEO satellites. Consequently, this may impose substantial signaling overhead for keeping the required information up-to-date in order to implement any shortest-path algorithm.  Therefore, finding the minimum-delay path in a distributed manner that solely relies on local information is desirable.

\begin{figure}[!htb]
	\centering
	\includegraphics[width=0.45\textwidth]{opt}
	\caption{Optimal substructure of the minimum-delay routing problem.}
	\label{fig:opt}
\end{figure}

To commence, we revisit the minimum-delay routing problem, which has a optimal substructure shown in Fig.~\ref{fig:opt}. Assume that the packet is currently located at the forwarding node (FN) $p_n$. Then, the optimal next hop minimizing
the overall E2E delay from the current FN to the DN $p_{\sf des}$ can be characterized by
\begin{equation}
p_{n+1} = \arg\min_{b\in \mathcal{B}} \{ D_{\sf link}(p_n, b) + D_{\sf que}(b) + D^*(b, p_{\sf des})\}, \label{eqn:opt}
\end{equation}
where $\mathcal{B}$ is set of nodes that are within the communication range of the FN, i.e. the set of neighbors, and $D^*(b, p_{\sf des})$ presents the minimum delay from each neighbor $b$ to the DN.

The optimal substructure indicates that if we know the delay from the FN to each of its neighbor, and also know the minimum delay from each of its neighbors to the DN, then the optimal next hop can be
readily obtained from~\eqref{eqn:opt}. Consequently, the optimal routing path can be obtained by determining the optimal next hop one by one until the packet reaches its DN.

\begin{figure*}[!htb]
	\centering
	\includegraphics[width=1\textwidth]{dnn}
	\caption{Offline training and online routing decision.}
	\label{fig:dnn}
\end{figure*}

In practice, the delay from the FN to each of its neighbor can be readily measured  by each neighbor and fed back to the FN almost instantly, while the minimum delay from each neighbor to the DN largely depends on  the global network topology, which is dynamic and it is not known at the FN. Fortunately, the satellite trajectories can be accurately predicted and the passenger flight trajectories are pre-planned before takeoff with fixed pattern and schedule. This suggests that the global network topology is strongly correlated with the local topology and such correlation can be learned from the quasi-predictable trajectories, which further motivates us to use a single-objective deep neural network (SO-DNN), for learning the mapping from the local geographical features to the minimum delay commencing from each of the FN's neighbors to the DN.

To reflect the local topology, the input features of the SO-DNN are designed as the coordinates (including the latitude, longitude and altitude) of the FN's neighbors and of the DN. The desired output of the SO-DNN is the minimum delay from the neighbors to the DN.

The training procedure of the SO-DNN is shown in Fig.~\ref{fig:dnn}, where the SO-DNN is trained in a supervised manner. The goal is to minimize the loss function composed of the mean-squared-error between the actual output  and the desired output of the SO-DNN. To generate the training samples, we can retrieve the position of each airplane and each satellite at each timestamp from the pre-planned flight trajectories as well as the predicted satellite trajectories, and then create a series of snapshots regarding the whole network topology.

For each snapshot of the network topology, the link-delay can be calculated based on the coordinate of each node and the delay model. As for the queuing-delay, since we aim for training the SO-DNN for embedding the network topology information, which is independent of the packet traffic, the queuing-delay is set to the same and constant for all the nodes during training. In this way, the total queuing-delay is actually determined by the number of hops along the path. Then, a standard shortest-path algorithm, such as the Floyd-Warshall algorithm,  can be used for computing the minimum delay between each possible source-destination pair as the training label (i.e. the desired output of the SO-DNN). In Fig.~\ref{fig:dnn}(a), we summarize the offline training phase.

Once the SO-DNN is sufficiently well trained, it can be copied to each airplane before takeoff for supporting their online routing decisions. During the online routing decision phase, the FN obtains the required coordinates from the on-board automatic dependent surveillance-broadcast (ADS-B) system, and gathers the input features for the SO-DNN. Then, from the output of the SO-DNN, the FN can obtain the estimate of the minimum delay $\hat D^*(b, p_{\sf des})$ from each of its neighbors to the DN, as shown in Fig.~\ref{fig:dnn}(b). Finally, by substituting the estimated minimum delay together with the delays measured by and fed back from each neighbor into the optimal substructure \eqref{eqn:opt}, the next-hop can be determined.

To improve the online adaptability further, we can recursively exploit the optimal substructure. Specifically, the minimum delay from each neighbor to the DN in~\eqref{eqn:opt} can be estimated by recursively using~\eqref{eqn:opt} instead of by directly using the SO-DNN. Then, by allowing each neighbor to estimate the minimum delay from its neighbor (i.e. the next-but-one neighbors of the FN) to the DN using the SO-DNN, and by letting each next-but-one neighbor feed back the link- and queuing-delays, additional real-time delay information can be exploited for improving the routing decisions.

\subsection{Multi-Objective Routing}
Next, we conceive multi-objective routing for simultaneously minimizing the E2E delay, maximizing the E2E throughput and maximizing the path-lifetime.

Typically, bio-inspired metaheuristics, such as multi-objective evolutionary algorithms (MOEAs), are harnessed for solving multi-objective optimization problems. However, MOEAs require global knowledge regarding the status (e.g., delay, throughput, lifetime) of every single possible link in the network for running the optimization, which is not feasible in large-scale SAGINs having high-dynamic network topology. Moreover, the Pareto optimality of MOEAs are not mathematically guaranteed.

Again, we resort to DL in order to exploit local information for solving the multi-objective routing problem without requiring global information. Similar to the minimum-delay routing problem, we first discover its optimal substructure. The multi-objective routing problem can be transformed into the following $\varepsilon$-constraint problem:
\begin{align}
\text{Objective:}~ &\text{Optimize routing to minimize the E2E delay} \nonumber \\
\text{Subject to:} ~&\text{E2E troughput larger than $\varepsilon_C$} \nonumber\\
& \text{and path-lifetime larger than $\varepsilon_L$}. \nonumber
\end{align}
It can be proved that all Pareto-optimal solutions can be obtained by solving the aforementioned $\varepsilon$-constraint problem with moderate complexity~\cite{chankong1983multiobjective,liu2021iot}. Furthermore, by varying the values of $\varepsilon_C$ and $\varepsilon_L$, all the Pareto-optimal solutions and hence the entire Pareto front can be found.

Then, our goal is to solve a series of constrained minimum-delay (single-objective) routing problems. Given the network topology, the queuing-delay of each node, and the delay/throughput/lifetime of each link, the constrained minimum delay routing problem can be solved effortlessly by modifying the standard shortest-path algorithm using the following procedure:
\begin{enumerate}
	\item Convert the constrained minimum-delay routing problem into a standard shortest-path equivalent problem by deleting all the links that have a throughput no higher than $\varepsilon_C$ or have a lifetime no longer than $\varepsilon_L$.
	\item Solve the converted  shortest-path problem.
\end{enumerate}

The above procedure can be regarded as adding an infinite delay penalty to the link that violates the throughput or lifetime constraint, and then solve the converted minimum-delay problem using the standard shortest-path algorithm. Therefore, similar to \eqref{eqn:opt}, we can derive the optimal substructure for determining the next hop as follows
\begin{align}
p_{n+1}  = &\arg\min_{b\in \mathcal{B}} \{D_{\sf link} (p_{n}, b) + D_{\sf que}(b) + D^*(b, p_{\sf des}, \varepsilon_C, \varepsilon_L)  \nonumber\\
& + \underbrace{ \lambda [\varepsilon_C - C(p_{n}, b)]^+ + \lambda[\varepsilon_C - C^*(b, p_{\sf des}, \varepsilon_C, \varepsilon_L)]^+}_{\text{Penalty for violation of throughput constraint}}  \nonumber\\
& + \underbrace{\lambda [\varepsilon_L - L(p_{n}, b)]^+ + \lambda[\varepsilon_L - L^*(b, p_{\sf des}, \varepsilon_C, \varepsilon_L)]^+}_{\text{Penalty for violation of lifetime constraint}}  \}. \label{eqn:opt2}
\end{align}
where $D^*(b, p_{\sf des}, \varepsilon_C, \varepsilon_L)$, $C^*(b, p_{\sf des}, \varepsilon_C, \varepsilon_L)$ and $L^*(b, p_{\sf des},$ $\varepsilon_C, \varepsilon_L)$ are termed as the constrained-delay, constrained-throughput, and constrained-lifetime, respectively. When there exists at least one path spanning from neighbor $b$ to the DN $p_{\sf des}$ satisfying the minimum-throughput constraint $\varepsilon_C$ and minimum-lifetime constraint $\varepsilon_L$, then the constrained-delay/throughput/lifetime represent the actual delay/throughput/lifetime achieved by the minimal delay path among the paths that satisfy the throughput and lifetime constraints. By contrast, when there is no path from $b$ to $p_{\sf des}$ that satisfies the throughput and lifetime constraint, then we opt for the delay/rate/lifetime achieved by the path from $b$ to $p_{\sf des}$ whose throughput and lifetime are closest to $\varepsilon_C$ and $\varepsilon_L$. Furthermore, $\lambda$ in \eqref{eqn:opt2} is the penalty coefficient for punishing the violation of the throughput or lifetime constraint, where $[x]^+ = \max\{x, 0\}$. The penalty term increases with the violation of E2E throughput or path-lifetime constraints, and hence the neighbors that are away from satisfying those constraints are less likely to be selected as the next hop.

In practice,   the link-delay and queuing-delay as well as the link-throughput and link-lifetime can be measured by the neighbor and fed back to the FN. Then, the optimal next-hop can be determined locally based on the optimal substructure~\eqref{eqn:opt2}, once the values of constrained-delay/throughput/lifetime of each neighbor are available at the FN.

Therefore, similar to SO-DNN, we use a multi-objective deep neural network (MO-DNN) for learning the mapping from the local geographical features to the constrained-delay/throughput/lifetime of each neighbor. In practice, the penalty coefficient $\lambda$ is set to a limited value, because when it is excessive (e.g., when approaching infinity) the optimal substructure~\eqref{eqn:opt2} becomes extremely sensitive to the estimation error of the constrained-delay/throughput/lifetime.

The input features of the MO-DNN are designed in Fig.~\ref{fig:dnn}. Compared to the SO-DNN, the speed and heading of the current node, of its neighbors and of the DN are also included into the features together with the node's coordinates, because the constrained-lifetime depends on those parameters. Moreover, the throughput and lifetime thresholds are also included into the input feature because they affect the values of the constrained-delay/throughput/lifetime.

Similar to the training of SO-DNN, the training procedure of the MO-DNN is shown in Fig.~\ref{fig:dnn}. In contrast to SO-DNN, we also sample different values of throughput and lifetime thresholds when generating the training sample.
The online routing decision phase is illustrated in Fig~\ref{fig:dnn}(b). In contrast to single-objective routing, by varying the value of $(\varepsilon_C, \varepsilon_L)$, multiple paths leading to the DN can be discovered as the solutions of the multi-objective routing problem. Similar to DL-aided minimum-delay routing, we can also improve the online adaptability of DL-aided multi-objective routing by recursively using the optimal substructure~\eqref{eqn:opt2}, which is not detailed here for conciseness.

\section{Performance Evaluation}
In this section, we quantify the benefits of integrating AANET with LEO SC by simulations based on real satellite, flight and shipping data.
\begin{table*}[!htb]
	\centering
	\scriptsize
	\caption{Simulation Parameters~\cite{3gpp2018study,vondra2018integration}}
	\setlength{\extrarowheight}{1.2pt}
	\begin{tabular}{|r|c|c|c|c|}
		\hline
		\textbf{Node} & \multicolumn{2}{c|}{\textbf{Satellite}} & \multicolumn{2}{c|}{\textbf{Airplane/Ship/GS}} \\
		\hline
		\textbf{Transmission mode} & To airplane/ship/GS & To satellite & To airplane/ship/GS/BS & To satellite \\ \hline
		\multirow{1.5}[2]{*}{\textbf{Carrier frequency}} & 20 GHz (Downlink)  & \multirow{1.5}[2]{*}{23 GHz} & \multirow{1.5}[2]{*}{14 GHz} & 20 GHz (Downlink)  \\
		& 30 GHz (Uplink) &       &       & 30 GHz (Uplink) \\ \hline
		\textbf{Total bandwidth} & 400 MHz & 400 MHz & 250 MHz & 400 MHz \\ \hline
		\textbf{Bandwidth per link} & 5 MHz & NA & 10 MHz & 5 MHz \\ \hline
		\textbf{Transmit power} & \multicolumn{2}{c|}{21.5 dBm} & 30 dBm & 33 dBm \\ \hline
		\textbf{Transmit antenna gain} & \multicolumn{2}{c|}{38.5 dBi} & 25 dBi & 43.2 dBi  \\ \hline
		\textbf{Receive antenna gain} & \multicolumn{2}{c|}{38.5 dBi} & 25 dBi & 39.7 dBi \\ \hline
		\multirow{1.5}[2]{*}{\textbf{Antenna gain-to-noise-temperature}} & \multicolumn{2}{c|}{\multirow{1.5}[2]{*}{13 dB/K}} & Airplane: 1.5 dB/K & Airplane: 16.2 dB/K \\
		& \multicolumn{2}{c|}{} & GS/Ship: 1.2 dB/K & GS/Ship: 15.9 dB/K \\
		\hline
	\end{tabular
	\label{tab:parameter
\end{table*

\subsection{Dataset}
The satellite traces  are generated based on their orbit information (more specifically, the two-line element set) of the Iridium-NEXT SC provided by the North American Aerospace Defense Command. The flight data was collected over the North Atlantic ocean on 29th of June in 2018. The status of each flight was recorded in the format of [timestamp, latitude, longitude, altitude, speed, heading] for every 10 s over the complete 24 hours of the selected date. The shipping data was also collected over the North Atlantic ocean in the same format from marinetraffic.com.

Since we only have the true flight traces during a single day, we have to generate multiple datasets of flight traces for testing outside of the training set. To reflect the mismatch between the flight traces used in training and testing, we randomly shift each true flight trace along the timeline in order to generate multiple synthetic flight datasets, respectively, used for training and testing. Specifically, the random shift is drawn from a Gaussian distribution with zero mean
and a standard deviation of 30 min. We further divide the training dataset into four time windows, each having six hours, where a MO-DNN was trained separately. In particular, for the simulations presented in this article, the MO-DNN was trained using the pre-planned flight traces and predicted satellite traces within a time
window of 12:00 -- 18:00 UTC. Once the MO-DNN is well-trained, the MO-DNN can be used for assisting the routing decisions during 12:00 -- 18:00 UTC in the testing dataset. We note that the training does not rely on ship distributions, because the MO-DNN is used for learning the constrained-delay/throughput/lifetime of each relaying neighbors that exclude the ships (i.e., packet source) as shown in Fig.~\ref{fig:dnn}(a).
\subsection{Simulation Settings}

The system configurations are listed in Table~\ref{tab:parameter}.
To generate the training labels, the queuing-delay is set to a constant 10~ms for each node during training. By contrast, to reflect the heterogeneous traffic load of each node during testing, the queuing-delay of each node is drawn from a [0, $\infty$)-truncated Gaussian distribution with a mean of 10 ms and a standard deviation of 5 ms. The packet size is 1 KBytes and the DN is set as the GS located at the Port of Southampton in the U.K. as labeled in Fig.~\ref{fig:path}.

\subsection{Hyper-Parameters for DL-aided Routing Algorithm}
Parameter sweeps are used for tuning our DL-aided routing algorithm. The main hyper-parameters are set as follows. The MO-DNN has three hidden layers, where the first two hidden layers have 300 neurons and the third hidden layer is split into three streams each having 100 neurons, for learning the constrained-delay/throughput/lifetime, respectively. We use ReLU as the activation function for all the hidden layers, and employ batch normalization, since each
dimension of the input has different units. Adam optimizer with an initial learning rate of 0.001 is used for training the MO-DNN and the mini-batch size is 1000. The throughput threshold $\varepsilon_C$ and lifetime threshold $\varepsilon_L$ are swept over [0, 70] Mbps with a step size of 5 Mbps and [0, 30] min with a step size of 5 min, respectively, in order to discover multiple paths. The penalty coefficient $\lambda$ is set to 10.

\subsection{Simulation Results}

In the following, we first use our modified shortest-path algorithm assuming perfect global information to find the Pareto-optimal paths for evaluating the potential of integrating AANET and LEO SC, and finally employ the proposed DL-aided multi-objective routing algorithm for finding the paths by considering a practical scenario when only local information is available.

\begin{figure}[!htb]
	\centering
	\includegraphics[width=0.5\textwidth]{path}
	\caption{A snapshot of the network at 15:00 UTC, Jun. 29, 2018.}
	\label{fig:path}
\end{figure}

In Fig.~\ref{fig:path}, we illustrate a snapshot of the network at 15:00 UTC. The position of each satellite, airplane, ship and GS are marked on the projected 2D map. In particular, we use different colors to distinguish the ships that can be successfully connected to the DN solely relying on the airplanes from those that cannot (with legend ``Ship with AC" and ``Ship without AC", respectively). It can be observed that not all the ships can be covered by the AANET.

\begin{figure*}[!htb]
	\centering
	\includegraphics[width=1\textwidth]{result}
	\caption{Simulation results, 00:00 -- 24:00 UTC, Jun. 29, 2018.}
	\label{fig:result}
\end{figure*}

Since there may exist more than one Pareto-optimal path from the SN to the DN, in Fig.~\ref{fig:path}, we further plot the minimum-delay path, maximum-throughput path, and maximum-lifetime path among all the paths found by our modified shortest-path algorithm. The SN is chosen from the north-east coast of US for analyzing the transatlantic communication scenario. We can see that the minimum-delay path is a two-hop path using a satellite as the relaying node, while the maximum-throughput path and the maximum-lifetime path are formed by airplanes. Specifically, the maximum-throughput path has the highest number of hops in order to reduce the distance of each link for maximizing the throughput of the path. We further plot the heading of the flights on the maximum-lifetime path to understand how the path is formed. We can see that the angle between the flight headings of adjacent nodes is small so that the lifetime of each link is maximized.

To characterize the merits of integrating AANET and LEO SC, we compare three systems, namely pure AANET, pure LEO SC, and integrated AANET with LEO SC in the following. We first plot the average performance of the path from each ship to the GS of the three systems during a whole day as shown in Fig.~\ref{fig:result}(a). It can be observed that the percentage of ships that can be connected to the GS solely relying on the AANET fluctuates during the day, because the coverage of AANET depends on the flight density, which varies during a whole day. The maximum coverage ratio of AANET can be as high as 40\% lasting about 8 hours. By contrast, LEO SC alone or combining LEO SC and AANET together can provide a 100\% coverage for the entire 24 hours. As for the E2E throughput and path-lifetime, we can see that pure AANET achieves an average maximum E2E throughput and path-lifetime that are similar to or even higher than that of LEO SC, although the coverage ratio of AANET is less than half of that of LEO SC. When integrating AANET with LEO SC, both the maximum E2E throughput and maximum path-lifetime can be improved up to 200\% on average\footnote{When there is no path between the SN and DN, the E2E throughput and path-lifetime are set to zeros.}.

To analyze the optimal tradeoff between multiple performance metrics, we respectively consider a pair of twin-objective routing problems for a clearer visualization. Specifically, we first minimize the E2E delay and maximize the E2E throughput simultaneously, and then minimize the E2E delay and maximize the path-lifetime simultaneously. In Fig.~\ref{fig:result}(b), we plot the corresponding Pareto-front of the paths between the SN and DN illustrated in Fig.~\ref{fig:path}. Observe that LEO SC is more suitable for reducing the E2E delay in the transatlantic scenario due to its lower numbers of hops, while the AANET is more suitable for increasing the E2E throughput and the path-lifetime, as a benefit of its lower link distance and longer duration of visibility. By integrating AANET and LEO SC, the Pareto front can be improved and the route can be selected more flexibly among the three metrics. When only relying on the local information, our proposed DL-aided multi-objective routing algorithm, with the legend ``AANET + LEO (DL)", can discover paths that achieve near-Pareto-optimal performance. By combining the E2E delay, E2E throughput and path-lifetime, we can further obtain and visualize a 3D-pareto front~\cite{liu2021iot}.

\section{Concluding Remarks}
In this article, we proposed a SAGIN framework that integrates AANETs formed by commercial passenger airplanes and LEO SC for connecting the ships in the North Atlantic Ocean. To satisfy the requirements of heterogeneous services and adapt to the dynamics of SAGIN, we further proposed a DL-aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results based on real satellite, flight and shipping data showed that the integrated network  achieves better coverage, lower E2E delay, higher E2E throughput as well as higher path-lifetime, and demonstrated that our DL-aided multi-objective routing algorithm is capable of achieving near-Pareto-optimal performance.
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Justia Patents Plural Pressure Varying Steps (e.g., Pressure Swing Adsorption, Etc.)US Patent for Carbon molecular sieve adsorbent Patent (Patent # 10,835,856)
Carbon molecular sieve adsorbent
Aug 14, 2018 - Air Products and Chemicals, Inc.
Disclosed herein are rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar. The processes use a carbon molecular sieve (CMS) adsorbent having an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of at least 0.2000 as determined by linear driving force model at 1 atma and 86° F.
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The present invention relates to a rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar using carbon molecular sieve (CMS) adsorbents.
PSA processes have long been used in the separation of the components of air. The use of carbon molecular sieve (CMS) adsorbents to separate varieties of gas mixtures has also been known for several decades (Walker, Jr., P. L., "Molecular Sieves", Mineral Industries, 1966).
The carbon molecular sieve (CMS) adsorbent is usually derived from processing distinct raw materials such as vegetal products (waste wooden materials, coconut shells, walnut shells, fruit kernels), mineral coals, waste mineral oils, waste resin etc. by variety of methods (U.S. Pat. Nos. 3,801,513; 4,458,022; Nakao, Y., "Control of Micro-pores of Molecular Sieving Carbon by Impregnation of Hydrocarbons and Heat Treatment", Presented at 20th Spring Chemical Industrial Convention, 1987), wherein ultimately the micro-pores are partially blocked with fine carbon particles which either narrow the pores or change the pore size distribution, thus changing the molecular sieving properties. Selecting an appropriate raw material which provides the initial micro-pore structure and maintaining of proper working parameters (binder type, carbonization time and temperature, hydrocarbon type and concentration for final cracking, cracking time and temperature, addition of inert flow during cooling and so on) are the key factors of getting a suitable CMS adsorbent for size selective separation.
U.S. Pat. No. 4,742,040 discloses several processes for manufacturing a carbon molecular sieve with improved capacity and selectivity that can be applied to the separation of nitrogen from air. U.S. Pat. No. 5,098,880 describes a two-step process of making a carbon molecular sieve having faster oxygen kinetics and high oxygen over nitrogen kinetic selectivity. U.S. Pat. No. 5,164,355 describes a method of producing a high capacity, high density carbon molecular sieve adsorbent which can be used as a host material for further modification to produce an efficient oxygen selective carbon molecular sieve. U.S. Pat. No. 5,972,834 discloses a method of manufacturing carbon molecular sieves suitable for separating oxygen and nitrogen from air involving mainly halogenation and dehalogenation treatments followed by a pore adjustment treatment using a thermally decomposable hydrocarbon (selected from benzene or toluene) on a carbonized charcoal (derived from coconut shell or phenol resin). U.S. Pat. No. 6,916,358 discloses several carbon molecular sieve adsorbents for separating nitrogen from a mixed gas containing nitrogen and oxygen.
Thus, as set out above, there are established methodologies for producing carbon molecular sieve (CMS) adsorbents and for "tuning" the properties of a CMS such that it shows high kinetic selectivity for oxygen over nitrogen or oxygen over argon. Selective micro-pores are introduced to the adsorbent where the pore mouth controls kinetics of oxygen, nitrogen, or argon. This pore mouth or pore entrance is therefore considered as the critical dimension for diffusion. Such micro-pores can exhibit very high selectivity, however this is generally achieved at the expense of overall adsorption rates. The rate of adsorption in CMS adsorbents is, therefore, inversely correlated with the kinetic selectivity. This behavior can be explained by the model presented by Nguyen and Do (Nguyen, C.; Do, D. D., "Dual Langmuir kinetic model for adsorption in carbon molecular sieve", Langmuir, volume 16, pages 1868-1873, 2000).
More recently, there has been considerable interest in the intensification of separation processes. In cyclic devices such as PSA and TSA, reducing cycle time is the primary means of achieving more production from a given quantity of material. However, as cycle time is reduced, cyclic devices usually face the problem of decreasing working capacity per cycle for the component of interest, decreasing product recovery, and increasing pressure drop.
Recent developments in PSA processes involve using complex structured adsorbents, such as relatively fast kinetically selective laminate adsorbent structures, to increase productivity. For example, U.S. Pat. No. 7,645,324 discloses a rotary PSA process using a laminated adsorbent for kinetic separation of gases. U.S. Pat. No. 7,645,324 teaches that the use of the kinetically selective laminate can allow for increased productivity, but in order to avoid the masking of kinetic selectivity by macropore mass transfer resistance, the macropore structure within the adsorbent layer should be as open as possible; i.e., the macropore void fraction should be relatively high. A problem in this respect, however, is that having a high void volume generally harms product recovery. Thus, it is to be expected that such improvements in productivity as may be obtained via use of such laminate structures would once again come at the expense of reduced product recovery. Also, when commercially available formed CMS adsorbents are milled to generate powder for creating laminate structure, the selective properties introduced by carbon deposition (distributed heterogeneously) is mostly lost, leaving a lower/unselective CMS with faster kinetics. Therefore, it seems impractical to make a CMS based laminate form of structured packing for a PSA process.
It is thus evident from the prior art that there is a trade-off between product recovery and process productivity; steps that are taken to increase product recovery are generally detrimental to process productivity, and vice versa.
Accordingly, there remains a need for PSA processes that have high process productivity whilst maintaining high product recovery.
The present inventors have found that using "fast" kinetically selective CMS adsorbents in a rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar surprisingly gives rise to improved process productivity whilst still maintaining high product recovery, as compared to the rates of productivity and recovery obtainable when using "slower" but more selective and higher capacity CMS adsorbents.
Several preferred aspects of processes according to the present invention are outlined below.
Aspect 1: A rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar, wherein the process utilizes a plurality of adsorption beds comprising a carbon molecular sieve (CMS) adsorbent having an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of at least 0.20 as determined by linear driving force model at 1 atma and 86° F., and wherein the process comprises subjecting each of the plurality of beds to a rapid PSA cycle comprising a feed step, at least one depressurization step, a purge step, and at least one re-pressurization step, the duration of the feed step being 60 seconds or less.
Aspect 2: The rapid cycle PSA process of Aspect 1, wherein the duration of the feed step is from 3 to 45 seconds.
Aspect 5: The rapid cycle PSA process of any one of Aspects 1 to 4, wherein the cycle time of the PSA cycle is 100 seconds or less.
Aspect 6: The rapid cycle PSA process of any one of Aspects 1 to 4, wherein the cycle time of the PSA cycle is 30 to 100 seconds.
Aspect 7: The rapid cycle PSA process of any one of Aspects 1 to 4, wherein the cycle time of the PSA cycle is 30 to 70 seconds.
Aspect 8: The rapid cycle PSA process of any one of Aspects 1 to 7, wherein the process utilizes 2 to 4 adsorption beds.
Aspect 9: The rapid cycle PSA process of any one of Aspects 1 to 7, wherein the process utilizes 2 adsorption beds.
Aspect 10: The rapid cycle PSA process of any one of Aspects 1 to 9, wherein the CMS has an O2/N2 kinetic selectivity of from 5 to 30 as determined by linear driving force model at 1 atma and 86° F.
Aspect 11: The rapid cycle PSA process of any one of Aspects 1 to 9, wherein the CMS has an O2/N2 kinetic selectivity of from 10 to 25 as determined by linear driving force model at 1 atma and 86° F.
Aspect 13: The rapid cycle PSA process of any one of Aspects 1 to 12, wherein the CMS has an O2/Ar kinetic selectivity of from 5 to 40 as determined by linear driving force model at 1 atma and 86° F.
Aspect 14: The rapid cycle PSA process of any one of Aspects 1 to 13, wherein the adsorbent beds comprise the CMS adsorbent in the form of random packing.
Aspect 15: The rapid cycle PSA process of Aspect 14, wherein adsorbent is dense loaded and held in place with a permeable hold down system.
Aspect 16: The rapid cycle PSA process of any one of Aspects 1 to 15, wherein the feed step is performed at a temperature of from about 0° F. to about 125° F.
Aspect 17: The rapid cycle PSA process of any one of Aspects 1 to 15, wherein the feed step is performed at a temperature of from about 20° F. to about 100° F.
Aspect 18: The rapid cycle PSA process of any one of Aspects 1 to 15, wherein the feed step is performed at a temperature of from about 20° F. to about 40° F.
Aspect 19: The rapid cycle PSA process of any one of Aspects 1 to 18, wherein the process is a rotary bed rapid cycle PSA process.
Aspect 20: The rapid cycle PSA process of any one of Aspects 1 to 18, wherein the process is a rotary valve rapid cycle PSA process.
Aspect 21: Use of a carbon molecular sieve (CMS) adsorbent in a rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar, wherein the CMS has an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of at least 0.20 as determined by linear driving force model at 1 atma and 86° F.
Aspect 22: The use of Aspect 21, wherein the CMS has an O2/N2 kinetic selectivity of from 5 to 30 as determined by linear driving force model at 1 atma and 86° F.
Aspect 23: The use of Aspect 21, wherein the CMS has an O2/N2 kinetic selectivity of from 10 to 25 as determined by linear driving force model at 1 atma and 86° F.
Aspect 25: The use of Aspect 21, wherein the CMS has an O2/Ar kinetic selectivity of from 5 to 40 as determined by linear driving force model at 1 atma and 86° F.
The present invention will hereinafter be described in conjunction with the appended figures wherein like numerals denote like elements:
FIG. 1 shows the operation of a 2-bed multi-step PSA cycle.
FIG. 2a is a graph showing normalized process performance in terms of feed to product ratio as function of cycle time for fast (414-02) and slow (414-04) CMS adsorbents using the 2-bed multi-step cycle presented in FIG. 1 for producing N2 (containing 4500 ppm O2) from air at 7.80 atma and 100° F.
FIG. 2b is a graph showing normalized process performance in terms of productivity as function of cycle time for fast (414-02) and slow (414-04) CMS adsorbents using the 2-bed multi-step cycle presented in FIG. 1 for producing N2 (containing 4500 ppm O2) from air at 7.80 atma and 100° F.
FIG. 3a is a graph showing normalized process performance in terms of feed to product ratio as function of cycle time for fast (414-02) and slow (414-04) CMS adsorbents using the 2-bed multi-step cycle presented in FIG. 1 for producing N2 (containing 45,000 ppm O2) from air at 7.80 atma and 100° F.
FIG. 3b is a graph showing normalized process performance in terms of productivity as function of cycle time for fast (414-02) and slow (414-04) CMS adsorbents using the 2-bed multi-step cycle presented in FIG. 1 for producing N2 (containing 45,000 ppm O2) from air at 7.80 atma and 100° F.
FIG. 4a is a graph showing normalized process performance in terms of feed to product ratio as function of cycle time for fast (414-02) and slow (414-04) CMS adsorbents using the 2-bed multi-step cycle presented in FIG. 1 for producing N2 (containing 4500 ppm O2) from air at 7.80 atma and 30° F.
FIG. 4b is a graph showing normalized process performance in terms of productivity as function of cycle time for fast (414-02) and slow (414-04) CMS adsorbents using the 2-bed multi-step cycle presented in FIG. 1 for producing N2 (containing 4500 ppm O2) from air at 7.80 atma and 30° F.
The ensuing detailed description provides preferred exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the ensuing detailed description of the preferred exemplary embodiments will provide those skilled in the art with an enabling description for implementing the preferred exemplary embodiments of the invention. Various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention, as set forth in the appended claims.
The articles "a" and "an" as used herein mean one or more when applied to any feature in embodiments of the present invention described in the specification and claims. The use of "a" and "an" does not limit the meaning to a single feature unless such a limit is specifically stated. The article "the" preceding singular or plural nouns or noun phrases denotes a particular specified feature or particular specified features and may have a singular or plural connotation depending upon the context in which it is used.
As used herein, "first," "second," "third," etc. are used to distinguish from among a plurality of steps and/or features, and are not indicative of the total number, or relative position in time and/or space unless expressly stated as such.
As used herein, the term "comprising" means consisting of or including.
As used herein, the phrase "and/or" placed between a first entity and a second entity includes any of the meanings of (1) only the first entity, (2) only the second entity, and (3) the first entity and the second entity. The term "and/or" placed between the last two entities of a list of 3 or more entities means at least one of the entities in the list including any specific combination of entities in this list. For example, "A, B and/or C" has the same meaning as "A and/or B and/or C" and comprises the following combinations of A, B and C: (1) only A, (2) only B, (3) only C, (4) A and B and not C, (5) A and C and not B, (6) B and C and not A, and (7) A and B and C.
Disclosed herein are "fast" kinetically selective carbon molecular sieve (CMS) adsorbents for use in rapid cycle pressure swing adsorption (PSA) processes for separating O2 from N2 and/or Ar. Also disclosed herein are rapid cycle pressure swing adsorption (PSA) processes for separating O2 from N2 and/or Ar that utilize a plurality of adsorption beds comprising said fast kinetically selective CMS adsorbents.
The rapid cycle PSA processes described herein, that use the disclosed fast CMS adsorbents for separating O2 from N2 or Ar, have surprisingly been found to provide improved process productivity whilst maintaining high product recovery. Additionally, it has been found that the rapid cycle PSA processes using the fast CMS adsorbents require less purge gas.
As used herein, a "fast" kinetically selective CMS adsorbent (also referred to herein as simply a "fast" CMS adsorbent) refers to a CMS adsorbent having an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of at least 0.20 as determined by linear driving force model at 1 atma and 86° F. More preferably, the fast CMS adsorbent has an O2 adsorption rate (1/s) of at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, or at least 0.30, as determined by linear driving force model at 1 atma and 86° F.
As used herein, a "slow" kinetically selective CMS adsorbent (also referred to herein as simply a "slow" CMS adsorbent) refers to a CMS adsorbent having an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of less than 0.20 as determined by linear driving force model at 1 atma and 86° F. for O2.
The linear driving force (LDF) model is a well-known model for determining adsorption rates from experimental uptake curves, and can be used to calculate both the adsorption rates of particular adsorbates on a particular adsorbent, and the resulting kinetic selectivity of an adsorbent for particular adsorbate pairings.
More specifically, the rates of adsorption of an adsorbate (e.g. O2, N2, or Ar) on an adsorbent is evaluated using a standard volumetric adsorption apparatus. The experiment consists of exposing an adsorbent sample, which is initially at vacuum and 303 K (86° F.), to a measured amount of the adsorbate at 1 atma (760 Torr/101 kPa) at the same temperature. The change in pressure is recorded as a function of time. The pressure versus time data is then subtracted from a similar pressure history using the same weight of quartz beads in the place of the adsorbent sample to obtain a plot of the amount of gas adsorbed as a function of time, also known as an uptake curve. The LDF model is then used to extract the adsorption rates of the adsorbate in units of inverse time (1/s) from the uptake curve. The kinetic selectivity of the adsorbent for a particular adsorbate pairing can in turn be determined from the ratio of the calculated adsorption rate for the two adsorbates individually. The analytical form of the model is given by the following equation (as also set forth in Table 1 in the literature by Sircar, S. and Hufton, J. R., "Why Does the Linear Driving Force Model for Adsorption Kinetics Work?", Adsorption 2000, 6, 137-147) where f(t) is the fractional uptake, k is the LDF mass transfer coefficient, a is the correction factor for the constant volume experiment and t is the time.
f  ( t ) = 1 - exp - kt  1 + α α
Where the process is for separating O2 from N2, the fast CMS adsorbent may preferably have an O2/N2 kinetic selectivity of from 5 to 30, 10 to 25, or 15 to 20 as determined by linear driving force model at 1 atma and 86° F. Where the process is for separating O2 from Ar, the fast CMS adsorbent may preferably have an O2/Ar kinetic selectivity of from 5 to 40. The fast CMS adsorbent may have any suitable adsorption capacity. For example it may have an adsorption capacity at equilibrium of between 0.2 to 0.4 mmol/g, as determined by standard isotherm measurement at 1 atma and 86° F.
As is well known, PSA processes involve subjecting each of the adsorbent beds used in the process to a PSA cycle comprising at least a feed step (also referred to as an adsorption step), at least one depressurization step, a purge step, and at least one re-pressurization step. In the feed step, a feed gas stream is introduced into and passed through the bed undergoing the feed step to adsorb one or more readily adsorbed components (i.e. O2 in the present case) from the feed stream, thereby producing a product gas stream exiting the bed that is depleted in the adsorbed component and enriched in the remaining components (i.e. N2 and/or Ar in the present case). In the depressurization steps the bed is depressurized down from the feed pressure during the feed step until a lower, purge pressure is reached, the depressurization steps typically including at least one depressurization equalization step (in which the bed is reduced in pressure by exhausting some of the gas in the bed and sending said gas to another bed that is undergoing an equalization re-pressurization step, thereby at least partially equalizing the pressure between the two beds), and a countercurrent depressurization or "blowdown" step, that typically takes place after said depressurization equalization steps, in which a gas enriched in the more readily adsorbed components is exhausted (typically in a counter-current direction) from the bed. In the purge step, a purge gas (typically some of the product gas produced in the adsorption step) is passed through the bed (typically in a counter-current direction), which is now at the aforementioned purge pressure, in order to remove remaining adsorbed components, this again resulting a gas stream enriched in said components being exhausted from the bed. Finally, in the re-pressurization steps the bed is pressurized back up to the feed pressure, the re-pressurization steps typically including at least one re-pressurization equalization step (in which the bed is increased in pressure by receiving gas from another bed that is undergoing an equalization depressurization step), and at least one step where the bed is re-pressurized with feed gas or product gas (or preferably both), the feed gas typically being introduced co-currently and the product gas counter-currently. As will be known, the cycle may include also other steps taking place between any of the steps described above.
As used herein in relation to a PSA cycle, a "co-current" step refers to a step in which gas flows into and/or out of the bed undergoing the step in a direction that is the same as the direction of flow of gas during the feed step. Likewise, a "counter-current" step refers to a step in which gas flows into and/or out of the bed undergoing the step in a direction that is the opposite of the direction of flow of gas during the feed step. Similarly, as used herein the "inlet" or "inlet" end of a bed refers to the inlet or end of the bed through which the feed gas enters during the feed step, and the "outlet" or "outlet" end of the bed refers to the outlet or end of the bed through which the product gas exits during the feed step.
As used herein, the term "rapid" PSA cycle means a PSA cycle in which the total duration of the feed step of the cycle is 60 seconds or less. Preferably the total duration of the feed step of the cycle is at least 3 seconds. Preferably the feed step has a total duration of from 3 to 45 seconds, or from 6 to 45 seconds, or 6 to 36 seconds.
The rapid PSA cycle preferably also has a cycle time of 100 seconds or less, the cycle time being the amount of time taken to complete one full set of steps of the PSA cycle (i.e. the feed step, the at least one depressurization step, the purge step, the at least one re-pressurization step, and any other steps as may be present in the cycle). More preferably the PSA cycle has a cycle time of from 30 to 100 seconds, or from 30 to 70 seconds.
The rapid cycle PSA process is carried out using a plurality of adsorption beds each comprising the fast CMS adsorbent. Any suitable number of beds may be used, but in certain embodiments the process uses from 2 to 4 adsorption beds only, or uses 2 beds only. In this regard, it should be noted that as used herein the term "adsorption bed" or "bed" refers to one or more vessels of adsorbent that are each undergoing each of the steps of the PSA cycle at the same time. Thus, a process that uses 2 beds has a first vessel or set of vessels that are all undergoing each step of the PSA cycle at the same time (this representing one of the beds), and has a second vessel or set of vessels that are all undergoing each step of the PSA cycle at the same time (this representing the other of the beds), the timing of the PSA cycle in the first vessel or set of vessels being however staggered from the timing of the PSA cycle in the second vessel or set of vessels such that each step of the PSA cycle in the first vessel or set of vessels does not start and finish at the same time as the start and finish of the corresponding step of the PSA cycle in the second vessel or set of vessels.
The adsorbent beds may be packed with the fast CMS adsorbent in any suitable form, but preferably comprise the CMS adsorbent in the form of random packing. The adsorbent should preferably be dense loaded and held in place with a permeable hold down system, such as a perforated plate with wire mesh facing the CMS to contain it and downward pressure applied, such as by springs. The dense loading prevents additional settling which would unload the springs. Dense loading may be achieved by methods known in the art, such as snowfall-type loading.
The feed step is typically performed at a temperature of about 0° F. to about 125° F., and more preferably at a temperature of about 20° F. to about 100° F. or about 20° F. to about 40° F. As used herein, the temperature at which the feed step is performed means the temperature of the feed gas being introduced into the adsorption bed, as measured at the inlet to the adsorption bed. Surprisingly, the present inventors have found that in the processes described and disclosed herein, performing the feed step of the process at a lower temperature (such as at about 30° F.) improves process productivity while at the same time maintaining or improving product recovery, which is contrary to known PSA systems. Performing the feed steps at such lower temperatures may be particularly beneficial where the feed gas is already available at a sub-ambient temperature (such as for example where the process is being used to separate oxygen and/or nitrogen from a crude argon stream obtained from a distillation column) and where significant cooling of the feed gas down to the desired temperature for the feed step is not required.
Any suitable feed pressure may be used during the feed step. For example, the pressure during the feed step, as measured at the inlet to the adsorption bed, may be from about 5 to about 12 atmospheres absolute.
The process may be performed using any suitable apparatus for carrying out rapid cycle PSA. Traditional switch valves will work up to a point. Preferably, however, the process is a rotary bed rapid cycle PSA process carried out using a rotary bed PSA apparatus (where the adsorption beds are mounted in a rotor that is positioned between and rotates relative to feed and product stator assemblies each containing stator plates that function as valve plates for switching the beds between PSA steps of the cycle), or is a rotary valve rapid cycle PSA process carried out using a rotary valve PSA apparatus (where conversely the adsorption beds are located in a fixed bed assembly and are switched between PSA steps by rotary feed and product valves that are known in the art).
Solely by way of example, an exemplary two-bed multi-step PSA cycle suitable for use with the processes of the present invention will now be described, with reference to FIG. 1. The cycle schedule is shown in FIG. 1 in tabular form. Each row of the table grid represents all the different cycle steps a given bed undergoes over the entire cycle, whereas a column of the grid represents which cycle step is being run by which bed at a particular unit time step. The total cycle time is the sum of all the individual unit time steps of a particular row. Note that in the cycle schedule, there is a highlighted section typically termed as "unit block" and within this block all the steps are being run by one of the multi-beds (Mehrotra et al., 2011). The number of repeated unit blocks is same as the number of beds. Typical cycle schedule formulation methodology can be found in the literature: "Mehrotra, A.; Ebner, A. D.; Ritter, J. A. Simplified Graphical Approach for Complex PSA Cycle Scheduling, Adsorption, 2011, 17 337-345". The salient features of all the cycle designs presented in FIG. 1 is summarized below.
The PSA cycle shown in FIG. 1 comprises a product and feed re-pressurization step (F1/RP1), a feed (F2, F3), a co-current depressurization step (CoD), co-current and dual equalization depressurization steps (EQD1, DEQD2, DEQD3), a counter-current depressurization step (CnD1, CnD2), a product purge step (PU1, PU2), a product re-pressurization step (RP2, RP3), a counter-current and dual equalization re-pressurization step (EQR1, DEQR2, DEQR3), and several idle steps (I1, I2, I3). In the product and feed re-pressurization (F1/RP1) step, the bed is pressurized to the highest-pressure level of the cycle with the addition of primary product (e.g. enriched N2) and feed gas (e.g. air) from top and bottom end of the bed, respectively. No product is withdrawn from the bed during this step of the cycle. Next, in the feed step (F2 and F3 representing phases of the same feed step) the introduction of feed gas is continued until the mass transfer zone of preferentially adsorbed component (i.e. O2) reaches the exit end of the bed without substantially breaking through it, and the un-adsorbed gas (e.g. purified N2) is discharged from the outlet end of bed as primary product. Then, in the co-current depressurization step (CoD), the feed flow is stopped and the bed pressure is reduced to first intermediate level through extracting product from top end of the bed. Then, in a co-current equalization depressurization step (EQD1), the bed is connected with a bed undergoing the counter-current equalization re-pressurization step (EQR1), and a portion of the void as well as desorbed gas is transferred from the product end of the bed undergoing EQD1 to the product end of the bed undergoing EQR1, thus partially equalizing pressure between the two beds and lowering the bed pressure in the bed undergoing EQD1 to second intermediate level at the end of this step. Next, in the dual end equalization de-pressurization step (DEQD2), more co-adsorbed gas as well as void gases are withdrawn from top and bottom of the bed undergoing said step, bringing the pressure in said bed down to a third intermediate level, the gases withdrawn from the top and bottom of the bed being sent to respectively the top and bottom of the bed undergoing the dual equalization re-pressurization step (DEQR2). Next, the dual end equalization de-pressurization is continued in step (DEQD3/CnD1) but with some of the counter-currently withdrawn depressurization step being taken as secondary product (i.e. an O2 enriched gas) rather than being used to re-pressurize another bed, this step further reducing the pressure of the bed to a fourth intermediate level. The bed is then counter-currently de-pressurized down to the lowest operating pressure in a counter-current depressurization step (CnD2), and purged at the lowest operating pressure in a purge step (PU1 and PU2), producing further secondary product. Thereafter, the bed is re-pressurized counter-currently with primary product gas in a counter-current re-pressurization step (RP2 and RP3). Following the re-pressurization step (RP2 and RP3), the column is further pressurized through pressure equalization re-pressurization steps (EQR1, DEQR2, and DEQR3) to bring the bed back to the pressure level for initiation and repetition of the cycle. Note that three idle steps (I1, I2, and I3) are also incorporated into the cycle schedule, during which the bed undergoing the idle step is isolated, the valves leading to it being in the closed position.
A 2-bed multi-step pressure swing adsorption (PSA) process cycle as shown in FIG. 1 was chosen to evaluate process performance indicators in terms of feed to product ratio and specific productivity using a fast (414-02) and a slow (414-04) carbon molecular sieve (CMS) adsorbents for separating nitrogen from air. The feed to product ratio is inverse of recovery, therefore, the lower the ratio, the better the recovery and vice versa. In other words, a lower feed to product ratio means lower power requirement for the compressor. The rates, kinetic selectivity, and equilibrium capacities of oxygen and nitrogen on the fast (414-02) and the slow (414-04) CMS adsorbents and characteristics of the adsorbent bed are summarized in Tables 1 and 2. Note that the oxygen uptake rate differs between the fast and slow CMS adsorbents by only 30%, but selectivity differs by a factor of 2.
The PSA process performance results, in terms of feed to product ratio and productivity for different N2 product purities (containing 4500 ppm and 45,000 ppm O2) and when using different feed step temperatures (100° F. and 30° F.), are summarized in FIGS. 2 (a and b), FIGS. 3 (a and b) and FIGS. 4 (a and b) for the fast (414-02) and slow (414-04) CMS adsorbents. In these figures, the feed to product ratio and productivity have been normalized by dividing by the representative values for 414-04 at the conditions given in Table 3 and those normalized values are plotted as a function of cycle time. For each adsorbent the PSA process parameters were individually optimized to generate these plots. In Table 3, the cycle times were 60 seconds for both 414-02 and 414-04 for the 100° F., 4500 ppm case and 50 seconds for the 100° F., 45,000 ppm case. For the 30° F. comparison, 414-02 was run at 90 seconds cycle time and 414-04 was run at 190 seconds. In all cases the pressure during the feed step, as measured at the inlet to the adsorption bed, was 7.80 atma.
As seen in FIGS. 2 to 4, the optimum cycle time for the fast CMS (414-02) was equal to or faster than that for the slow CMS (414-04) at all N2 product purities (containing different amount of O2) and temperatures. The surprising feature with the fast CMS (414-02) is that significantly better productivity (34.5% better at 100° F. and 4500 ppm O2, 23.2% better at 100° F. and 45,000 ppm O2 and 95.4% better at 30° F. and 4500 ppm O2) at similar recovery (i.e., similar feed to product ratio) can be maintained at all product purities and temperatures considered here versus the slow CMS (414-04), even though the kinetic selectivity and equilibrium capacity of the fast CMS is considerably lower than the slow CMS. This means that the bed size, and therefore the capital cost, can be reduced with the use of fast carbon molecular sieve in a PSA process.
Another surprising feature for the fast CMS (414-02) was that at cold operating temperature, much higher productivity can be obtained while still maintaining similar or better recovery (i.e. lower feed to product ratio). For example, at 7.80 atma bed pressure and 4500 ppm O2 in product stream, the normalized feed to product ratio and normalized productivity are 0.979 and 1.954, respectively at 30° F. operating temperature and 90 seconds cycle time. Even at 100° F. and 60 seconds cycle time with the same operating pressure and product O2 purity, the normalized feed to product ratio for 414-02 was only 1.6% higher than 414-04 and the normalized productivity for 414-02 was still 34.5% higher than for 414-04. The slow CMS (414-04) gave about 26% reduction in productivity with about 12% gain in recovery going from 100° F. to 30° F. feed temperature. In contrast, the fast CMS (414-02) showed a 6.7% increase in productivity with a 16% gain in recovery going from 100° F. to 30° F. feed temperature. The comparative results can be found in Table 3 as well as in FIGS. 2 (a and b) and 4 (a and b).
The amount of purge gas needed to effectively regenerate the bed is surprisingly lower for the production of moderate purity nitrogen by PSA using the fast CMS (414-02), even though it has lower selectivity than 414-04. Even with the lower purge gas, the process performance indicators for the fast CMS are significantly better than those of the slow CMS. A summary of purge to feed ratio (in terms of ACF ratio) is provided in Table 3 for different product purities (containing different amount of O2) and temperatures for the fast and slow CMS adsorbents. For the fast CMS, the purge to feed ratio is about 43% lower at 100° F. and about 8% lower at 30° F. versus the slow CMS adsorbent. Note that part of the primary product enriched in slower diffusing component (nitrogen in case of air separation by CMS based PSA) is used to execute a countercurrent purge step (PU1 and PU2 steps as shown in FIG. 1) following the counter-current blowdown step (CnD1 and CnD2 steps as shown in FIG. 1) at low pressure. The purge step is necessary to make sure that the bed is sufficiently clean in order to produce primary product at certain purity during the production step.
As mentioned previously, the uptake rates for all the diffusing components on the fast CMS (414-02) are faster than those on the slow CMS (414-04). The fast CMS adsorbent considered in these studies was approximately 25% faster than the slow CMS absorbent, but 58% less selective (based on kinetic selectivity) than the slow CMS adsorbent (Table 1). Nevertheless, in the rapid cycle process the fast CMS adsorbent not only provided a higher specific productivity (as might be expected in view of its faster uptake rates) but it achieved this with no/minimum loss in recovery (i.e. similar feed to product ratio) despite its lower selectivity, as can be seen from Table 3.
The enhancement in specific productivity with no/minimum loss in recovery is very surprising, as it runs contrary to that which was previously known in the art. In a study by Schork et al. (1993), it was mentioned that productivity would increase as the uptake rates are increased, but that the maximum obtainable recovery would decrease. Table 4 (data obtained from Schork et al.) summarizes the effect of overall rate constant of O2 and N2 on process performance indicators (recovery and productivity) on a carbon molecular sieve adsorbent from this study, which clearly shows that increasing productivity is to the detriment of product recovery.
While the principles of the invention have been described above in connection with preferred embodiments, it is to be clearly understood that this description is made only by way of example and not as a limitation of the scope of the invention.
Summarizes linear driving force (LDF) model based O2 rate constants and O2 over N2 kinetic selectivity for different carbon molecular sieve adsorbents at 1 atma and 86° F.
Equilibrium LDF rate constant O2/N2 kinetic capacity CMS Description for O2 (1/s) selectivity (mmol/gm) 414-02 Fast CMS 0.2204 16.21 0.2559 414-04 Slow CMS 0.1761 38.28 0.3138 414-05 Fast CMS 0.2613 27.30 0.2822 414-06 Fast CMS 0.3426 24.08 0.3344
TABLE 2 Summarizes adsorbent and bed characteristics for a fast (414-02) and a slow (414-04) carbon molecular sieve adsorbents.
Adsorbent Characteristics
Adsorbent type (—) 414-02 414-04 Adsorbent diameter (ft) 0.0043 0.0032 Total void fraction (—) 0.5311 0.6451 Interstitial void fraction (—) 0.3019 0.3336 Bulk density (lb/ft3) 41.92 42.89 LDF rate constant at 1.0 atma & 86° F. for Oxygen (1/s) 0.2204 0.1761 Nitrogen (1/s) 0.0136 0.0046 Argon (1/s) 0.0092 0.0027 O2/N2 kinetic selectivity 16.21 38.28
Bed Characteristics
Bed length (in) 40 40 Bed inside diameter (in) 4 4
Summarizes purge requirement (in terms of purge to feed ratio) for the fast (414-02) and slow (414-04) CMS adsorbents at different operating temperatures and product O2 purities. The feed to product ratio and productivity values have been normalized by the 414-04 values for the same temperature, pressure, and product O2 conditions. The adsorbent and bed characteristics are summarized in Table 2. The 2-bed multi-step cycle presented in FIG. 1 is used for the evaluation.
Purge Temper- Cycle Product Feed to to Ad- Pressure ature time O2 product Produc- feed sorbent (atma) (° F.) (s) (ppm) ratio tivity ratio
414-02 7.80 100 50 45000 1.014 1.232 0.043 60 4500 1.016 1.345 0.049 30 90 4500 0.979 1.954 0.126 414-04 7.80 100 50 45000 1.000 1.000 0.076 60 4500 1.000 1.000 0.087 30 190 4500 1.000 1.000 0.138
summarizes the effect of overall rate constant of O2 and N2 process performance indicators (recovery and productivity) on a carbon molecular sieve adsorbent. The rate constants are multiplied by a single factor, therefore in all cases, the kinetic selectivity is the same as that of the base case. The results are taken from "Schork, J. M.; Srinivasan, R.; Auvil, S. R. Shortcut Computational Method for Designing Na PSA Adsorbents, Ind. Eng. Chem. Res., 1993, 32, 2226-2235".
Performance relative Factor for rate to base case (%)
constant Feed step time (s) Recovery Productivity
1.0 120 1.5 90 −2 +30 2.0 70 −5 +58 2.5 60 −7 +77 3.0 50 −10 +100 4.0 45 −11 +118
1. A rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar, wherein the process utilizes a plurality of adsorption beds comprising a carbon molecular sieve (CMS) adsorbent having an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of at least 0.20 as determined by linear driving force model at 1 atma and 86° F., and wherein the process comprises subjecting each of the plurality of beds to a rapid PSA cycle comprising a feed step, at least one depressurization step, a purge step, and at least one re-pressurization step, the duration of the feed step being 60 seconds or less.
2. The rapid cycle PSA process of claim 1, wherein the duration of the feed step is from 3 to 45 seconds.
5. The rapid cycle PSA process of claim 1, wherein the cycle time of the PSA cycle is 100 seconds or less.
6. The rapid cycle PSA process of claim 1, wherein the cycle time of the PSA cycle is 30 to 100 seconds.
7. The rapid cycle PSA process of claim 1, wherein the cycle time of the PSA cycle is 30 to 70 seconds.
8. The rapid cycle PSA process of claim 1, wherein the process utilizes 2 to 4 adsorption beds.
9. The rapid cycle PSA process of claim 1, wherein the process utilizes 2 adsorption beds.
10. The rapid cycle PSA process of claim 1, wherein the CMS has an O2/N2 kinetic selectivity of from 5 to 30 as determined by linear driving force model at 1 atma and 86° F.
11. The rapid cycle PSA process of claim 1, wherein the CMS has an O2/N2 kinetic selectivity of from 10 to 25 as determined by linear driving force model at 1 atma and 86° F.
13. The rapid cycle PSA process of claim 1, wherein the CMS has an O2/Ar kinetic selectivity of from 5 to 40 as determined by linear driving force model at 1 atma and 86° F.
14. The rapid cycle PSA process of claim 1, wherein the adsorbent beds comprise the CMS adsorbent in the form of random packing.
15. The rapid cycle PSA process of claim 14, wherein adsorbent is dense loaded and held in place with a permeable hold down system.
16. The rapid cycle PSA process of claim 1, wherein the feed step is performed at a temperature of from about 0° F. to about 125° F.
17. The rapid cycle PSA process of claim 1, wherein the feed step is performed at a temperature of from about 20° F. to about 100° F.
18. The rapid cycle PSA process of claim 1, wherein the feed step is performed at a temperature of from about 20° F. to about 40° F.
19. The rapid cycle PSA process of claim 1, wherein the process is a rotary bed rapid cycle PSA process.
20. The rapid cycle PSA process of claim 1, wherein the process is a rotary valve rapid cycle PSA process.
21. Use of a carbon molecular sieve (CMS) adsorbent in a rapid cycle pressure swing adsorption (PSA) process for separating O2 from N2 and/or Ar, wherein the CMS has an O2/N2 and/or O2/Ar kinetic selectivity of at least 5 and an O2 adsorption rate (1/s) of at least 0.20 as determined by linear driving force model at 1 atma and 86° F.
22. The use of claim 21, wherein the CMS has an O2/N2 kinetic selectivity of from 5 to 30 as determined by linear driving force model at 1 atma and 86° F.
23. The use of claim 21, wherein the CMS has an O2/N2 kinetic selectivity of from 10 to 25 as determined by linear driving force model at 1 atma and 86° F.
25. The use of claim 21, wherein the CMS has an O2/Ar kinetic selectivity of from 5 to 40 as determined by linear driving force model at 1 atma and 86° F.
3801513 April 1974 Munzner et al.
4458022 July 3, 1984 Ohsaki et al.
4548799 October 22, 1985 Knoblauch et al.
4742040 May 3, 1988 Ohsaki et al.
4938939 July 3, 1990 Kuznicki
5098880 March 24, 1992 Gaffney et al.
5164355 November 17, 1992 Farris et al.
5240474 August 31, 1993 Auvil
5447557 September 5, 1995 Golden
5704964 January 6, 1998 Kaneko et al.
5871565 February 16, 1999 Leavitt
5972834 October 26, 1999 Ohsaki et al.
6051050 April 18, 2000 Keefer et al.
6451095 September 17, 2002 Keefer et al.
6565628 May 20, 2003 Xu et al.
6767386 July 27, 2004 Kawai et al.
6916358 July 12, 2005 Nakamura et al.
7645324 January 12, 2010 Rode et al.
7651549 January 26, 2010 Whitley
7670408 March 2, 2010 Ota
8470395 June 25, 2013 Khiavi et al.
9359203 June 7, 2016 Kokubu et al.
9403118 August 2, 2016 Sakamoto et al.
9669349 June 6, 2017 Lau et al.
9895646 February 20, 2018 Monereau et al.
9925514 March 27, 2018 Coe et al.
20020121193 September 5, 2002 Baksh et al.
20060169142 August 3, 2006 Rode et al.
20140060326 March 6, 2014 Sundaram
20140076164 March 20, 2014 Monereau et al.
20170144101 May 25, 2017 Tanaka et al.
20170239640 August 24, 2017 Coe et al.
202337689 July 2012 CN
103787285 May 2014 CN
101318634 December 2014 CN
10-1659760 September 2016 KR
2008/089564 July 2008 WO
Babicki, M., et al.; "PSA Technology Hits the Fast Lane"; Chemical Processing; PutmanMedia; Aug. 2003; pp. 1-6.
Delgado, J.A., et al.; "Numerical Simulation of a Three-Bed PSA Cycle for the Methane/Nitrogen Separation with Silicalite"; Elsevier; Separation and Purification Technology; vol. 77; 2011; pp. 7-17.
Yavary, M., et al.; "The Effect of Number of Pressure Equalization Steps on the Performance of Pressure Swing Adsorption Process"; Elsevier, Chemical Engineering and Processing; vol. 87; 2015; pp. 35-44.
U.S. Appl. No. 15/718,620, filed Sep. 28, 2017 entitled Processes Using Improved RHO Adsorbent Compositions with first named inventor Shubhra Jyoti Bhadra.
U.S. Appl. No. 15/718,467, filed Sep. 28, 2017 entitled RHO Adsorbent Compositions, Methods of Making and Using Them with first named inventor Magdalena M. Lozinska.
Nakano, Y.;"Control of Micro-pores of Molecular Sieving Carbon by Impregnation of Hydrocarbons and Heat Treatment", Presented at 20th Spring Chemical Industrial Convention; 1987.
Chagger, H.K., et al.; "Kinetics of Adsorption and Diffusional Characteristics of Carbon Molecular Services"; Pergamon; Elsevier Science Ltd.; Carbon vol. 33, No. 10; pp. 1405-1411; 1995.
Chen, Y.D., et al.; "Diffusion of Oxygen, Nitrogen and Their Mixtures in Carbon Molecular Sieve"; AlChE Journal; Apr. 1994; vol. 40, No. 4; pp. 577-585.
Kumar, R., et al.; "A Versatile Process Simulator for Adsorptive Separations"; Pergamon; Elsevier Science Ltd.; Chemical Enginering Science; 1994; vol. 49, No. 18; pp. 3115-3125.
Reid, C.R., et al.; "Adsorption of Gases on Carbon Molecular Sieves Used for Air Separation. Spherical Adsorptives as Probes for Kinetic Selectivity"; Langmuir; 1998; 14; pp. 2415-2425.
Reid, C.R., et al.; "Adsorption of Gases on Carbon Molecular Sieve Used for Air Separation. Linear Adsorptives as Probes for Kinetic Selectivity"; Langmuir; 1999; 15; pp. 3206-3218.
Schork, J.M., et al.; "A Shortcut Computational Method for Designing N2 PSA Adsorbents"; Ind. Eng. Chem. Res.; 1993; vol. 32; pp. 2226-2235.
Walker, P.L., Jr.; "Molecular Sieves"; Mineral Industries; Jan. 1966; pp. 1-7.
Nguyen, C. et al, "Dual Langmuir kinetic model for adsorption in carbon molecular sieve", Langmuir, vol. 16, pp. 1868-1873, 2000.
Sircar S. et al, "Why Does the Linear Driving Force Model for Adsorption Kinetics Work?", Adsorption 2000, 6, 137-147.
Mehrotra, A. et al, "Simplified Graphical Approach for Complex PSA Cycle Scheduling", Adsorption, 2011, 17 337-345.
Filed: Aug 14, 2018
Assignee: Air Products and Chemicals, Inc. (Allentown, PA)
Inventors: Roger Dean Whitley (Allentown, PA), Shubhra Jyoti Bhadra (Macungie, PA), Erdem Arslan (Macungie, PA), Yong Cao (Shanghai), Timothy Christopher Golden (Nantes)
Primary Examiner: Frank M Lawrence, Jr.
Current U.S. Class: Plural Pressure Varying Steps (e.g., Pressure Swing Adsorption, Etc.) (95/96)
International Classification: B01D 53/047 (20060101);

redpajama_set_name: RedPajamaCommonCrawl


text:
Lake Oroville Hydro Power Plant That Can Serve Up To 800,000 Homes Goes Offline Due To Drought
By CBS13 Staff August 5, 2021 at 11:41 pm
Filed Under:California Drought, Lake Oroville
OROVILLE (CBS13) — Due to falling levels at Lake Oroville, one of California's largest reservoirs, a hydroelectric power plant that can serve up to 800,000 homes has been taken offline as extreme drought conditions continue to impact the state.
This is the first time the power plant has been taken offline due to a drought. Karla Nemeth, the director of the California Department of Water Resources (DWR), made the announcement in a statement Thursday, which read in part:
"DWR anticipated this moment, and the state has planned for its loss in both water and grid management. We have been in regular communication about the status of Hyatt Powerplant with the California Independent Service Operator (CAISO) and the California Energy Commission and steps have been taken in anticipation of the loss of power generation."
The water in Lake Oroville is pumped through underground facilities to generate electricity, which can power up to 800,000 homes when it's running at full capacity. The Hyatt power plant is the fourth largest hydroelectric energy producer statewide.
"DWR will continue to focus on reservoir operations and water storage management at Lake Oroville to preserve as much water in storage as possible. DWR will use the River Valve Outlet System to release some water from the base of Oroville Dam to maintain river temperature requirements and outflows to the Feather River," Nemeth said.
State officials and experts have been predicting all summer that the power plant would be taken offline by August. California has already experienced extremely dry conditions and severe heatwaves several times this year, putting strain on the state's power grid amid another brutal fire season.
"Falling reservoir levels are another example of why it is so critical that all Californians conserve water. We are calling on everyone to take action now to reduce water use by 15 percent, to preserve as much water supply in storage as possible should we experience another dry year. We are all in this together," Nemeth said.
The DWR also said it is delivering 5% of requested water supplies to State Water Project contractors to provide water for agricultural and urban use. Storage at San Luis Reservoir in Merced County is being used to meet these deliveries. The DWR said these deliveries are expected to have little impact on the amount of water released or stored at Lake Oroville.
CBS13 Staff
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Gintaras Čiurlionis (Ministry of the Interior) presented the development of computer forensics in the EU, while Gintaras Donela and Linas Markūnas from Forensic Science Center of Lithuania reviewed forensics investigations in Lithuania. AccessData representatives Abdeslam Afras and Faisal Habib introduced and demonstrated AccessData advanced computer forensics tools.
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Science and business cooperation to find effective solutions and create next generation products is vital for reaching these targets. Therefore, BAIP is going to cooperate with Kaunas University of Technology and other academic institutions where digital forensics courses are going to be delivered.
Dr. Vilius Benetis looked at computer forensics and law enforcement needs from the perspective of Lithuanian cyber security programme. He said: 'the need to ensure IT security in both private and governmental organisations is very high. Therefore our clients want to receive up to date information about the innovative IT security solutions and increase their capabilities. We are very happy that we were chosen to represent AccessData solutions, the leaders in the field of computer forensics, to our clients'.
AccessData representative Abdeslam Afras rejoiced that the interest in the initiatives of cyber security is so high: 'It shows that the topic is extremely important', he said: 'The field of digital forensics is quite complicated and demands a lot of expertise. Therefore, we are very happy that in Lithuania we have found partners like BAIP who are capable of doing more than simply reselling our solutions - they can also provide clients with comprehensive consultancy, taking into account every individual case'.
AccessData are the pioneers in the field of computer forensics and the creators of computer forensics software, FTK; working in the field for over 20 years. Modern AccessData computer forensics labs allow handling of massive data sets, utilization of a distributed workforce, collaboration and consultations with experts who are not directly involved in forensics. More than 130,000 professionals, working in the field of law enforcement, consultancies, and government agencies use AccessData broad-spectrum software that enables them to perform digital investigations of any kind.
UAB BAIP provides critical IT infrastructure services and the Cyber Defence Plan, assisting clients in consolidation, virtualization, security and management of their IT environment by means of BAIP proprietary methodology CIMF (Critical Infrastructure Maintenance Framework). The main specialization areas are IT infrastructure design, security, virtual environments, IT services management, storage, backup and recovery, and cloud computing. BAIP expertise includes design and implementation of highly scalable distributed systems, high performance interconnected networks and large scale parallel and distributed storage systems.
UAB BAIP is a subsidiary to BAIP group together with NRD AS and UAB NRD. BAIP group is a group of companies that specialises in the field of critical information systems for governments and large organisations. Together with its sister company NRD AS, UAB BAIP operates and implements projects in the areas of Central and Eastern Europe (CEE) and East Africa.
In 2009 UAB BAIP was presented with a title of 'the most progressive service-sector company' in the annual 'Žinių ekonomikos forumas' ('Knowledge Economics Forum') awards.


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BridgeWee was made without any resources at hand. The only thing available to help BridgeWee was the power of social media, and it came through for BridgeWee. What applied for BridgeWee applied for WebAble, as well as every other brand WebAble has successfully worked with. Research- thorough research backed by data, engagement with audience and building a loyal community around the brand with just enough passion- that's what WebAble does, and has done brilliantly every time.
It's not a company that simply offers you thousands of likes. WebAble offers you to stand out amongst your competitors, offers you creative content that brands you, and offers you the security that only number-driven strategizing can. WebAble campaigns are built on foundations of concrete research, data, analysis, creativity and most importantly a talented team.
The WebAble team is headed by three individuals- they are different people tied to together by the same passion,energy and willingness to utilize the internet in Bangladesh as never before. They don't do things halfway- it's all or nothing for them. This trio is the head,heart and soul of WebAble and by looking at them and how they do things anyone can envision why WebAble is the game-changer in the digital marketing scene.
The founder of Webable, Ovick, initially started an educational institution, Bridgewee, back in 2009 when resources were scarce. He literally built the BridgeWee brand online! His passion for digital helped him end up in the world of online marketing. By using the power of social media, he managed to successfully reach his target, conducted market research, engaged with audience and most importantly built a strong and loyal community around BridgeWee.
This is what Ovick does but who is he? A marketing major from the University of Dhaka looking to do something outside the box, Ovick knows his way around marketing and branding. He saw he could do something new and unprecedented in Bangladesh- help small companies out by enabling the web and use a strategy- a data driven and creative strategy. The vision here remains to use unconventional marketing methods and take businesses to the next level- online.
Shadab, one of the co-founders of Webable, is a risk taker. He left behind his old job and dived into WebAble in hopes of using his skills to make it big.
Shadab knows how to write, he mastered the plethora of online and offline tools to gather data, he figured out the secret of creating great content- everything required to set WebAble on its way to success. Shadab never stops learning- online branding, content marketing, web programming- he does it because he loves to do it.
Shadab currently studies Finance in University of Dhaka and is now focused to communicate the true value of internet technology through Webable and be great value addition to the Bangladeshi online experience.
His journey started out back in '09- his belief that Bangladeshi youth had more to offer and his explorations led him co-found WebAble. He has a multi-faceted knowledge of the internet and utilizes it to get the best results for online businesses.
Anis, co-founder, is the solution finder. Spending his whole life in Saudi Arabia until university, he came to Bangladesh with a vision to do something that would put Bangladesh on the map. He struggled by himself in Dhaka, yet he moved on with a passion and drive that he would succeed in his venture. He may have hit a road-bump with a small web development start-up that didn't make it but that's who Anis is- he doesn't give up, and he still hasn't stopped working tirelessly to help small brands compete with international ones in a way that makes a statement. His extensive knowledge of not only the market, but of people,numbers, businesses, trends, ideas and what not ties together the team to bring it all online.
Ovick, Shadab and Anis indeed are three different people with three different skill sets- yet what makes them work successfully together is the vision to see Bangladeshi businesses go online and do it in style. Like the internet, ambition is equally effective in cumulating the rebel streak in hackers, charm of salespeople and the recklessness of entrepreneurship.
Would I Buy The Amazon fire Phone?


Vic Duppa-Whyte was out of this world. He was a gifted paper engineer in England that created some of the most inventive pop-up designs in the 1980's. In fact, David Carter and other folks that worked with him through Intervisual Books noted that Vic's creations were so advanced at the time that they could not be produced. Luckily, there is a half dozen titles were released that bare his name. All of them are amazing. Today we check out the The Space Shuttle Action Book from 1983. Published by Random House and produced by Carvajal in Columbia, this introduction to the space shuttle houses many inventive pop-ups.
The NASA space shuttle program had just begun when the book came out and Vic does a great job right out of the gate with creating the excitement of exploration of space. As we open the first page, one pulls a tab and the anticipation builds with a four second countdown before the rockets fire and the pop-up shuttle lifting off the page. Another clever construction is a pop-up cockpit complete with functional joystick that changes the forward view as the shuttle tilts left or right with the aid of a single black thread.
The space shuttle program may be ending this year, due to soaring costs and privatized ventures, but this book will continue to send me to the moon for years to come.