Designing Waste Minimization Alternatives for Batch Processes Using an Intelligent Simulation-Optimization Framework
Multi-scale and/or multi-disciplinary approach to process-product innovation
Integrated Methodologies for Process Development (T3-7)
Keywords: Sustainable development, Intelligent system, Process synthesis, Multi-objective decision making, Simulated annealing
The issue of environmental sustainability has prompted the batch chemical industries to switch from end-of-pipe treatment to waste minimization as top priority in tackling the pollution problem. However, the multi-scale nature of batch operation has posed challenges in implementing waste minimization analysis. Firstly, batch processes often involve chemistry that is complex and poorly understood. As a result, waste minimization potential through suppression of the byproducts or recycling of the valuable components may be overlooked. Secondly, as batch operation delivers its product in discrete amounts, the flowrate, composition and other waste stream properties could vary considerably with the execution of each operation within the production cycle time. This would complicate the overall strategy for employing effective measures to the plant. Thirdly, the substantial involvement of process operators makes batch operation highly susceptible to unplanned waste generation caused by human errors. All these factors motivate a systematic framework, one that is robust and efficient for evaluating design alternatives for waste minimization opportunities in the plants.
In this paper, we introduce a novel simulation-optimization framework that integrates different process systems engineering (PSE) methodologies such as P-graph scheme, Douglas’ hierarchical design approach, WAR algorithm and multi-objective optimization algorithm to solve the waste synthesis problem for the batch process plants. This framework extends our previously BATCH-ENVOPExpert, an expert system that identifies sources of waste as well as inefficient process operations (Halim and Srinivasan, 2006). We propose integrating the expert system with process simulator and mathematical optimization technique. The objective is to derive more comprehensive and cost-effective waste minimization solutions by capitalizing on the heuristic methods and rules pertinent to a knowledge-base system and at the same time the modeling and simulation characteristics of a process simulator. This is implemented as follows. First, qualitative reasoning is performed by the expert system to identify the sources of wastes and heuristic design alternatives for minimizing them are then proposed. This is followed by quantitative analysis using the simulation engine of the process simulator. We use gPROMS for this purpose, although other commercial simulators could also be used. In this way, the effects of changes to the design and operating condition on the amount and composition of the generated wastes and economic implications can be assessed. In the next step, the interpretation of these simulation results is performed and used to drive an extrinsic multi-objective optimization routine. This is done using heuristic search algorithm of simulated annealing approach to obtain the Pareto sets that highlight the trade-offs between environmental impact and plant economic. We illustrate this framework by testing it on a well-known literature batch operation case study involving reaction and distillation.
References:
Halim, I., & Srinivasan, R. (2006). Systematic Waste Minimization in Chemical Processes: Part III. Batch Operations. Industrial and Engineering Chemistry Research, 45, 4693.
See the full pdf manuscript of the abstract.
Presented Thursday 20, 15:20 to 15:40, in session Integrated Methodologies for Process Development (T3-7).
