An Expert System-Based Approach For Improving Energy Efficiency Of Chamber Cleaning Machines

Abstract

Increased transparency and domain expertise are often prerequisites for identifying energy savings potentials and improving energy efficiency in manufacturing systems. Small and medium-sized enterprises pursuing a reduction in CO2 emissions are especially faced with challenges from the complexity of process data and limited domain expertise. Against this background, this paper presents an expert system for preliminary energy diagnostics using automated energy analysis of production machines and providing measures for improving energy efficiency. Due to their significant energy consumption and increasing importance along various process chains, the use case is developed for chamber cleaning machines. A knowledge base is combined with artificial intelligence techniques for data processing to reveal efficiency potentials based on machine load profiles. The knowledge base created by experts assigns domain-specific information to the automatically processed input data. Key performance indicators are then utilized for internal and external benchmarking and quantification of energy potential, narrowing down promising energy efficiency measures. The suitability of the proposed approach is demonstrated by applying the expert system to two different chamber cleaning machines.