(223b) Integrated Approach to Waste, Utility, and Energy Minimization in Batch Processes

The issue of environmental sustainability has prompted the batch chemical industries to minimize their waste generation. At the same time, increasing pressure to reduce the energy consumption has called for efficient utilization of energy during the batch operation. However, the multi-scale nature of batch operation has posed complications in implementing effective waste and energy minimization strategies. In contrast to continuous processes, batch process operations are intrinsically time-dependent, and thus techniques from continuous processes cannot be directly applied.

Given the complex and multidisciplinary nature of waste and energy minimization analysis of batch plants, a systematic way of identifying and evaluating suitable design alternatives is thus essential. Previously, we have described BATCH-ENVOPExpert (BEE), an intelligent waste minimization support tool for qualitative waste minimization analysis of batch plants. BEE has been successfully tested on a number of case studies including propylene glycol [1], diphenol ether [2], and Dyelate production process [3]. In all case studies, BEE has been found to identify almost all the waste minimization suggestions identified by the human expert and some others not mentioned in the human's results.

In this paper, we extend the BEE methodology by introducing a novel simulation-optimization framework that integrates different process systems engineering (PSE) techniques such as process graph (P-graph) scheme, Douglas' hierarchical design approach, WAR algorithm, heat exchanger network (HEN) design and multi-objective optimization algorithm to derive cost-effective waste and energy minimization solutions. We propose integrating the BEE expert system with gPROMS dynamic simulator and GAMS optimizer for a comprehensive analysis that capitalizes on the heuristic methods and rules in the expert system, the modeling and simulation capabilities of a process simulator, and optimization capabilities. We illustrate this framework using a well-known literature batch operation case study involving reaction and distillation [4], wherein we identify operations alternatives that result in minimal generation of process wastes, minimal consumption of the washing liquids for cleaning the equipment, and minimal usage of energy.

References:

[1] Halim, I. & Srinivasan, R. Design Synthesis for Simultaneous Waste Source Reduction and Recycling Analysis in batch processes. ESCAPE-15, Barcelona, Spain. [2] Halim, I. & Srinivasan, R. An Expert System for Cleaner Production in Batch Processes. PRES-05, Taormina, Italy. [3] Halim, I. & Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part III. Batch Operations. Industrial and Engineering Chemistry Research 45(13), 4693-4705, 2006. [4] von Watzdorf, R., Naef, U.G., Barton, P.I., & Pantelides, C.C. Deterministic and Stochastic Simulation of Batch/Semicontinuous Processes. Computers and Chemical Engineering 18(supplement), 343-347, 1994.