EXAMPLE REFLECTION: LEARNING LAUNCH

CHALLENGE AND SELECTION    In my current role as an Associate Technical Service and Development Scientist, I work with  clients in the Mining industry recommending and implementing chemistry solutions to add  lasting value. I am particularly focused on improving business performance, fostering innovation  and meeting sustainability standards.    The case I present here is a New Product Development Case.  Details on the problem statement,  hypothesis tested and products selected to test are presented in Table 1.      The tool I chose is “Learning Launch”, which in my industry we like to call it “Pilot Testing”.  Once  my team and I identified clearly the customer problem, we proposed a hypothesis that would  solve such problem.  Then, among a spectrum of chemicals available across my company, we  chose two that might potentially solve the problem and validate our hypothesis.    My exercise here today shows how I used “Learning Launch” or “Pilot Testing” to test our  hypothesis with the product we proposed at the customer site.    Table 1. Overview of the Problem.    OVERVIEW  Problem  Customer is a mining operator that recovers Copper and/or Molybdenum  from polymetallic ore. Customer’s current problem is low recoveries of  Molybdenum. The customer believes that the high content of clay in this  particular ore is not allowing a proper flotation process. This statement is  supported by literature review.

Hypothesis  Tested

The metal flotation is being spoiled because the clay is interfering a proper  modification of the surface of the metal. A formulated dispersant, whose  goal is to inhibit the agglomeration of clay in the metal surface, would  maintain the clay particles in suspension allowing the flotation process to  undergo normally.

Solutions to Test Two chemicals: Product XUR1 and Product XUR2.

Key  Performance  Indicators (KPI’s)

•  Molybdenum recovery and grade.

•  Copper recovery and grade.

APPLICATION

The first part of the Application of the tool is to state a “Design of Experiments”, that is, a plan  for the “Learning Launch”.  The Design Experiments looked like it:

Learning Guide – Design of Experiments

Goal:  The purpose of this Proof of Concept study was to:

• Test hypothesis exposed previously in Table 1.  • Screen several dosages of product to determine optimum.

Assumptions to be tested:

• Recovery of Molybdenum is enhanced.  • No negative side effects on the recovery of copper or other areas of the process.  • Implementing the technology will generate profits to the client.

Success metric for Learning Launch:  The following metrics are sufficient to measure the success of the pilot.

• Recovery of Molybdenum is enhanced.  • Recovery of copper remains the same or is improved.   • Price to sell Molybdenum along with the cost of the product.

Products  Dispersants Dosage   (g/ton)

Key   Performance   Indicators

Product XUR1 25, 50, 100 , 200, 300 Cu and Mo recovery and concentrate grade

Product XUR2 25, 50, 100 , 200, 300 Cu and Mo recovery and concentrate grade

INSIGHTS AND APPROACH    We ran the pilot at the customer site, with their process and materials.  The technical  performance is summarized in Figures 1 (Recovery of Molybdenum) and Figures 2 (recovery of  Copper):

Figure 1. Recovery of Molybdenum.

Figure 2. Recovery of Copper

In order to determine the economic analysis, the assumptions were:

• The customer will be able to sell all extra Molybdenum that is recovered.  • The customer sells Molybdenum at the trading price of 7.26 USD/lb  • Technical improvement seen in pilot does not change with time.  • The most important source of cost for this implementation is the cost of chemical.

And the increase in annual revenue and the cost of the implementation are depicted in Figure 3.  Figure 3.

The main insights are:

• Hypotheses and Assumptions Validated. Both products improve recovery of

Molybdenum - up to 20 % points - in comparison with no product implemented.  The  grade of molybdenum is also enhanced. Under certain conditions, both products slightly  improve or do not affect the recovery and grade of copper.

• In order to maximize the recovery and grade of Molybdenum and, increase slightly the

recovery and grade of copper, it is recommended to use Product XUR2 within 50-100  g/ton.

• Both products do not interfere with processes downstream and meet environmental

regulations (EH&S and Product stewardship check).

Approach: Customer satisfied with the performance. Moved to commercialization.


EXAMPLE REFLECTION : LEARNING LAUNCH CHALLENGE AND SELECTION In my current role as an Associate Technical Service and Development Scientist , I work with clients in the Mining industry recommending and implementing chemistry solutions to add lasting value . I am particularly focused on improving business performance , fostering innovation and meeting sustainability standards . The case I present here is a New Product Development Case . Details on the problem statement , hypothesis tested and products selected to test are presented in Table 1 . The tool I chose is “ Learning Launch ” , which in my industry we like to call it “ Pilot Testing ” . Once my team and I identified clearly the customer problem , we proposed a hypothesis that would solve such problem . Then , among a spectrum of chemicals available across my company , we chose two that might potentially solve the problem and validate our hypothesis . My exercise here today shows how I used “ Learning Launch ” or “ Pilot Testing ” to test our hypothesis with the product we proposed at the customer site . Table 1 . Overview of the Problem . OVERVIEW Problem Customer is a mining operator that recovers Copper and/or Molybdenum from polymetallic ore . Customer ’s current problem is low recoveries of Molybdenum . The customer believes that the high content of clay in this particular ore is not allowing a proper flotation process . This statement is supported by literature review . Hypothesis Tested The metal flotation is being spoiled because the clay is interfering a proper modification of the surface of the metal . A formulated dispersant , whose goal is to inhibit the agglomeration of clay in the metal surface , would maintain the clay particles in suspension allowing the flotation process to undergo normally . Solutions to Test Two chemicals : Product XUR1 and Product XUR2 . Key Performance Indicators ( KPI ’s ) • Molybdenum recovery and grade . • Copper recovery and grade . APPLICATION The first part of the Application of the tool is to state a “ Design of Experiments ” , that is , a plan for the “ Learning Launch ” . The Design Experiments looked like it : Learning Guide – Design of Experiments Goal : The purpose of this Proof of Concept study was to : • Test hypothesis exposed previously in Table 1 . • Screen several dosages of product to determine optimum . Assumptions to be tested : • Recovery of Molybdenum is enhanced . • No negative side effects on the recovery of copper or other areas of the process . • Implementing the technology will generate profits to the client . Success metric for Learning Launch : The following metrics are sufficient to measure the success of the pilot . • Recovery of Molybdenum is enhanced . • Recovery of copper remains the same or is improved . • Price to sell Molybdenum along with the cost of the product . Products Dispersants Dosage ( g / ton ) Key Performance Indicators Product XUR1 25 , 50 , 100 , 200 , 300 Cu and Mo recovery and concentrate grade Product XUR2 25 , 50 , 100 , 200 , 300 Cu and Mo recovery and concentrate grade INSIGHTS AND APPROACH We ran the pilot at the customer site , with their process and materials . The technical performance is summarized in Figures 1 ( Recovery of Molybdenum ) and Figures 2 ( recovery of Copper ): Figure 1 . Recovery of Molybdenum . Figure 2 . Recovery of Copper In order to determine the economic analysis , the assumptions were : • The customer will be able to sell all extra Molybdenum that is recovered . • The customer sells Molybdenum at the trading price of 7.26 USD / lb • Technical improvement seen in pilot does not change with time . • The most important source of cost for this implementation is the cost of chemical . And the increase in annual revenue and the cost of the implementation are depicted in Figure 3 . Figure 3 . The main insights are : • Hypotheses and Assumptions Validated . Both products improve recovery of Molybdenum - up to 20 % points - in comparison with no product implemented . The grade of molybdenum is also enhanced . Under certain conditions , both products slightly improve or do not affect the recovery and grade of copper . • In order to maximize the recovery and grade of Molybdenum and , increase slightly the recovery and grade of copper , it is recommended to use Product XUR2 within 50 - 100 g / ton . • Both products do not interfere with processes downstream and meet environmental regulations ( EH&S and Product stewardship check ) . Approach : Customer satisfied with the performance . Moved to commercialization .