This review provides an overview of the present state of the art in applying AI techniques to the work of Chemical and Process Engineers. Such work is described, where necessary, in order to make this paper understandable to an audience outside the process engineering community. The first section discusses the nature of chemical engineering design and draws the important distinction between process design and plant design. The complete design cycle is covered a stage at a time and AI applications and issues identified. The section finishes with a discussion of integrated or concurrent design, which is the ultimate aim of much current research. The second section discusses AI applications in process operation. This covers process monitoring, alarm processing and fault diagnosis. The third section discusses, in some depth, design stage loss prevention, that is the design of plants to be safe and reliable. Safety is an integral part of design, but the inclusion of a separate section is appropriate as the qualitative and judgemental nature of many safety related problems has encouraged the use of AI techniques. Applications of AI planning to the problem of automatic operating procedure synthesis and applications of qualitative simulation to chemical engineering problems are also discussed in this section.

Construction Engineering and Management is a broad area of civil engineering practice which includes: (1) planning and engineering of the temporary facilities for construction sites; (2) management of the construction process; and (3) rehabilitation, repair and maintenance of engineered facilities, where the traditionally distinct design and construction roles of participants have become merged. The broad scope of this field, combined with the empirical nature of many facets of construction engineering and management practice, have led to a significant amount of expert system activity in this domain.

The theme of this paper is to review work carried out in the area of applying expert systems and artificial intelligence in electrical power engineering. It surveys expert systems and artificial-intelligence-based algorithms developed for solving decision problems in power network and generator fault diagnosis, reactive power and voltage control, restoration of power supply, determination of load circuits for shedding in under-frequency load shedding schemes, generator scheduling, allocation of loads and circuits, load-flow planning, generation expansion planning and distribution expansion planning.