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Multi-Objective Decision Making in Pharmaceutical Waste Management: A Combinatorial Approach

This study explores a combinatorial process synthesis framework for optimizing waste management in multi-stage pharmaceutical plants under uncertainty. It focuses on energy sourcing costs, specifically comparing external sources, gas-fired, and coal-fired boilers. The research outlines the challenges of plant-wide waste management, presenting a case study that leverages superstructure optimization to balance cost, environmental impact, and risk. Key findings emphasize the importance of flexible design solutions and the trade-offs involved in decision-making processes, ultimately providing insights for effective management strategies.

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Multi-Objective Decision Making in Pharmaceutical Waste Management: A Combinatorial Approach

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  1. SOLUTION APPROACH – COMBINATORIAL PROCESS SYNTHESIS Energy from External Sources ($9.0/MBTU) Energy from Gas-Fired Boilers ($1.2/MBTU) Energy from Coal-Fired Boilers ($4.0/MBTU) (1)-(3) : Pareto Frontiers Total Operating Cost (103 $Yr) (2) (1) (3) Global Warming Impact (Co2 Emissions, Ktons/Yr) Total Operating Cost (103 $Yr) (1) (2) (3) Global Warming Impact (Co2 Emissions, Ktons/Yr) QUANTIFICATION OF RISK UNDER UNCERTANTY Multi-Objective Decision Making and Risk Management in Pharmaceutical Plants Andres Malcolm, Aninda Chakraborty and Andreas A. Linninger Laboratory for Product and Process Design, Department of Chemical Engineering, University of Illinois at Chicago e-mail: amalco1@uic.edu CHALLENGES: PLANT WIDE WASTE MANAGEMENT SUMMARY CASE STUDY WASTE 5 TREE MULTI-STAGE PHARMACEUTICAL PLANT UNDERWOOD’S MODEL 17% Acetonitrile 83% Propanol 99% Propanol 1% Acetonitrile P4_X29 P4_X76 (b) 94% Acetonitrile 1% Acetone 3% Propanol W5 63% Acetonitrile 37% Acetone (a) P4_X1 P4_X13 99.9% Acetonitrile 63% Acetone 36%Acetonitrile 1%Propanol P4_X10 TREATMENT DATABASE P4_X8 99% Acetone 1% Acetonitrile Detail P4_( X1 – X8) P4_X5 SUPERSTRUCTURE GENERATION Superstructure of waste 5. (OnR – Onsite Recycle; INC – Incineration; REU – Reuse; EVA – Evaporation; IonE – Ion Exchange; SCR – Scrubber; WAO - Wet Air Oxidation; BIO  - Biological Treatment; LEA - Leaching; LF – Landfill; ATM – Atmosphere; SEW – Sewer). ENERGY SOURCE INFLUENCE DESIGN UNDER UNCERTANTY SUPERSTRUCTURE OPTIMIZATION Trace Single policy in 3 Scenarios Onsite Boiler reduces cost, increases Emissions SUMMARY AND CONCLUSION • Deterministic design leads to flowsheets that are vulnerable to uncertainty. • Combinatorial Process Synthesis include the impact of waste load variations on waste management • Solution strategies to rigorously obtain • Minimum Risk Design • Stochastic Flexible Design • Exhaustive enumeration of design policies is prohibitly expensive for large # of wastes • Superstructure optimization is therefore necessary. • Superstructure Optimization - Modest execution time ( < 1 minute) to obtain mathematically sound guidelines for decision-makers. TRADEOFF BETWEEN COST, ENVIRONMENTAL IMPACT AND RISK CASE STUDY - TRADEOFF BETWEEN COST AND FLEXIBILITY

  2. SOLUTION APPROACH – COMBINATORIAL PROCESS SYNTHESIS Energy from External Sources ($9.0/MBTU) Energy from Gas-Fired Boilers ($1.2/MBTU) Energy from Coal-Fired Boilers ($4.0/MBTU) (1)-(3) : Pareto Frontiers QUANTIFICATION OF RISK UNDER UNCERTANTY Multi-Objective Decision Making and Risk Management in Pharmaceutical Plants Andres Malcolm, Aninda Chakraborty and Andreas A. Linninger Laboratory for Product and Process Design, Department of Chemical Engineering, University of Illinois at Chicago e-mail: amalco1@uic.edu CHALLENGES: PLANT WIDE WASTE MANAGEMENT CASE STUDY WASTE 5 TREE SUMMARY MULTI-STAGE PHARMACEUTICAL PLANT UNDERWOOD’S MODEL 17% Acetonitrile 83% Propanol 99% Propanol 1% Acetonitrile P4_X29 P4_X76 (b) 94% Acetonitrile 1% Acetone 3% Propanol W5 63% Acetonitrile 37% Acetone (a) P4_X1 P4_X13 99.9% Acetonitrile 63% Acetone 36%Acetonitrile 1%Propanol P4_X10 P4_X8 TREATMENT MAP FOR DECISION MAKING 99% Acetone 1% Acetonitrile Detail P4_( X1 – X8) P4_X5 SUPERSTRUCTURE AND ITS PROPERTIES Superstructure of waste 5. (OnR – Onsite Recycle; INC – Incineration; REU – Reuse; EVA – Evaporation; IonE – Ion Exchange; SCR – Scrubber; WAO - Wet Air Oxidation; BIO  - Biological Treatment; LEA - Leaching; LF – Landfill; ATM – Atmosphere; SEW – Sewer). ENERGY SOURCE INFLUENCE DESIGN UNDER UNCERTANTY SUPERSTRUCTURE OPTIMIZATION SUMMARY AND CONCLUSION • Deterministic design leads to flowsheets that are vulnerable to uncertainty. • Combinatorial Process Synthesis include the impact of waste load variations on waste management • Solution strategies to rigorously obtain • Minimum Risk Design • Stochastic Flexible Design • Exhaustive enumeration of design policies is prohibitly expensive for large # of wastes • Superstructure optimization is therefore necessary. • Superstructure Optimization - Modest execution time ( < 1 minute) to obtain mathematically sound guidelines for decision-makers. TRADEOFF BETWEEN COST, ENVIRONMENTAL IMPACT AND RISK CASE STUDY - TRADEOFF BETWEEN COST AND FLEXIBILITY

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