Expert systems: Opportunities in the minerals industry

Abstract Since the inception of expert systems in the early 1980's their application has spread to a wide variety of industries. There are now thousands of successful expert system applications providing real benefit. The medical profession was the first to utilize expert systems technology through the now classic MYCIN system. During these formative years the minerals industry was quick to identify the potential of these systems. PROSPECTOR for ore body identification was the first expert system associated with the minerals industry. Since the early years the minerals industry has embraced the new technology with an unexpected fervour. At present expert systems can be found in most areas of the mineral processing, extractive metallurgy and mining industries. Many applications are off-line or stand alone in nature, although increasingly on-line and real-time systems are appearing. The aim of this paper is to review the current status of expert systems, both within the minerals industry and in other spheres of industry. Expert systems associated with applications as diverse as the nuclear power industry and brewing are examined. Through this review future developments and applications within the minerals industry are suggested and explored.

[1]  D. Sbarbaro,et al.  Using an intelligent supervisory system to simulate automatic operation in a crushing plant , 1989 .

[2]  W. Stange The Control of Mineral Processing Plants Using Neural Network Techniques , 1992 .

[3]  Jonathan W. Doughty,et al.  Expert System for Minefield Site Prediction. Phase 3 , 1988 .

[4]  Walter F. Truszkowski Intelligent tutoring in the spacecraft command/control environment , 1988 .

[5]  R. A. Bearman,et al.  The development of a comminution index for rock and the use of an expert system to assist the engineer in predicting crushing requirements , 1990 .

[6]  Harold M. Heggestad Knowledge-based operation and management of communications systems , 1988 .

[7]  J. J. Cilliers Neural Networks for Steady-State Process Modelling and Fault Diagnosis , 1992 .

[8]  R. Milne,et al.  Predicting faults with real-time diagnosis , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[9]  P. Tucker,et al.  Expert Systems: Their role in mineral processing in the UK , 1989 .

[10]  Kazuya Asano,et al.  Neural Network Model for Recognition of Characters Stenciled on Slabs , 1992 .

[11]  J. Leung,et al.  Advances in expert system applications in mineral processing , 1989 .

[12]  Daniel Hodouin,et al.  MODELLING AND CONTROL OF MINERAL PROCESSING PLANTS USING NEURAL NETWORKS , 1992 .

[13]  Henrik Saxén,et al.  An Expert System for Continuous Steel Casting Using Neural Networks , 1992 .

[14]  Paul Harmon,et al.  Creating Expert Systems for Business and Industry , 1990 .

[15]  T. Inoue,et al.  Mineral Process Control by Neural Network , 1992 .

[16]  Karl A. Smith,et al.  Mastering Engineering Concepts by Building an Expert System. , 1983 .

[17]  Robert M Hayashi Framework for Evolutionary Development of an Autonomous Expert System for Acoustically Identifying Classifications of Vessels , 1989 .

[18]  R. Milne Case studies in condition monitoring , 1990 .

[19]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[20]  W. Harper The development of expert systems for process monitoring in British Nuclear Fuels , 1991 .