Mohammed Almuzian, Samer Mheissen, Mark B Wertheimer, Haris Khan
Global survey to assess preferences for attending virtual orthodontic learning sessions: Pre-webinar settings
Journal of orthodontics
organic contaminants polycyclic aromatic compounds pacs occurring industrial effluents persist wastewater also undergo transformation toxic mobile substituted heterocyclic products treatment thus predicting occurrence pacs heterocyclic derivatives hpacs coking wastewater utmost importance reduce environmental risks receiving water bodies hpacs monitored sampling analysis characterisation techniques costly timeconsuming study propose 3 distinct kernelbased machine learning ml models predicting pacs including substituted hpacs alkylated pacs occurring coking wastewater using routinely measured wastewater quality data input models predicted occurrence 14 hpacs final effluent r2 083 performance assessment regression model based support vector machine svr showed logarithmic error male 046 square error rmse 0073 ngl comparatively knearest neighbor random forest models showed r2 075 076 respectively hpac prediction model exploration feature analysis revealed superior predictability svr model based higher weightage 81 towards input variables dissolved organic carbon total ammonia could capture underlying secondary transformations likely occurring treatment plant based partial dependence plots ammonia levels higher 120 mgl doc levels 5060 mgl indicative higher hpacs dissolved coking effluent work highlights capability kernelbased ml models capturing nonlinear wastewater chemistry offer tool monitoring trace organic contaminants released coking wastewater effluents