Development of emission orientated production control strategies using Fuzzy Expert Systems, Neural Networks and Neuro-Fuzzy approaches

In industrial production processes, materials and different forms of energy are provided, converted, stored and transported. Environmental impacts can be identified at any stage of the energy and material flow process. Due to the fact that production units and processes are interconnected with energy and material flows, it is of special interest to develop production control mechanisms, which control the energy and material streams so that available resources are utilised most efficiently and reduce emissions and by-products caused by the production process. Methodical production control strategies can be based on optimal algorithms, production rules or methods of machine learning. Due to the complexity of real production systems, it is advisable to use heuristic approaches. In order to analyse the behaviour of different control strategies, the developed systems are verified by an exemplary production system from the textile industry, consisting of a dye house, a hydro-power, a boiler house, and a flue gas neutralisation facility. A verification of the developed systems shows that Fuzzy Expert Systems, Neural Networks, and Neuro-Fuzzy approaches can be applied for the controlling of energy and material flows, taking into account economic and emission orientated goals. The selection of a certain approach mainly depends on the structure of the available production knowledge.