ARTIFICIAL-INTELLIGENCE IN PROCESS ENGINEERING - CURRENT STATE AND FUTURE-TRENDS

Recent advances in artificial intelligence have changed the fundamental assumptions upon which the progress of computer-aided process engineering (modeling and methodologies) during the last 30 yr has been founded. Thus, in certain instances, numerical computations today constitute inferior alternatives to qualitative and/or semi-quantitative models and procedures which can capture and utilize more broadly-based sources of knowledge. In this paper it will be shown how process development and design, as well as planning, scheduling, monitoring, analysis and control of process operations can benefit from improved knowledge-representation schemes and advanced reasoning control strategies. It will also be argued that the central challenge coming from research advances in artificial intelligence is "modeling the knowledge", i.e. modeling: (a) physical phenomena and the systems in which they occur; (b) information handling and processing systems; and (c) problem-solving strategies in design, operations and control. Thus, different strategies require different forms of declarative knowledge, and the success or failure of various design, planning, diagnostic and control systems depends on the extent of actively utilizable knowledge. Furthermore, this paper will outline the theoretical scope of important contributions from AI and what their impact has been and will be on the formulation and solution of process engineering problems.