ARTIFICIAL-INTELLIGENCE APPROACH TO SYNTHESIS OF A PROCESS FOR WASTE MINIMIZATION

One of the most effective ways of minimizing wastes from their sources is to design an environmentally clean process which provides a satisfactory level of controllability. In the present work, a systematic module-based synthesis approach is developed to design such a process with minimal waste generation. This approach is featured by adding the dimension of structural controllability to the conventional capital and operating cost functions, and elaborating waste minimization strategies as constraints. Due to insufficient information and incomplete data regarding the mechanism of waste generation at the process design step, artificial intelligence techniques are utilized to represent waste minimization strategies and evaluate structural controllability. The efficacy of the proposed approach to waste minimization is illustrated by synthesizing a cost-effective and highly controllable process capable of minimizing phenol containing waste streams in an oil refinery.