An Intelligent System for Identifying Waste Minimization Opportunities in Chemical Processes

摘要

Pollution prevention is one of the major issues facing the chemical industry worldwide. Increasing environmental awareness and regulations have put pressure on the chemical industry for implementing waste minimization at the source rather than relying on end-of-pipe treatment. Conducting a waste minimization review is time-consuming, expensive and labor- and knowledge-intensive. An automated system that performs waste minimization analysis would reduce the time and effort required for a thorough review and thus is attractive. In this paper, we propose a knowledge-based system, called ENVOPExpert, that can detect and diagnose waste generation in any chemical process, and identify process-specific waste minimization options. ENVOPExpert has been tested on an industrial hydrocarbon separation process. We also present ENVOPExpert's results for the case study and compare it with waste minimization options suggested by a team of experts.