OPTIMAL CONTROL OF WASTEWATER TREATMENT PLANTS VIA INTEGRATED NEURAL NETWORK AND GENETIC ALGORITHMS

The successful operation of wastewater treatment plants involves many uncertain factors. Not only the physical and chemical properties of wastewater streams but also the complexity of biological mechanism would significantly influence the performance of treatment process. Due to the rising concerns of environmental and economic impacts, improved control algorithms, using artificial intelligence technologies, have gradually received wide attention in the scientific community. This paper develops a genetic algorithm-based neural network for the assistance of intelligent controller design. An industrial wastewater treatment plant in Taiwan verified the applicability of such a methodology. The hybrid intelligent control technology applied in this paper is suitable to many other types of wastewater treatment plants by a slightly modified concept.     

Fuzzy logic control for a parallel hybrid hydraulic excavator using genetic algorithm

In this paper, fuzzy logic control is put forward in a parallel hybrid hydraulic excavator for the purposes of better energy distribution and higher fuel economy. A mathematical model of parallel hybrid hydraulic excavator is presented in detail, and the parameters of components and overall system are listed and analyzed. The fuzzy logic controller is then designed to cope with energy distribution and management. To achieve better equivalent fuel consumption, genetic algorithm is implemented to fine-tune the membership functions. The control effects are compared between different control strategies, e.g. rule-based control and fine-tuned fuzzy logic control. The results indicate that hybrids with the proposed strategy can improve fuel economy for the excavator without sacrificing any system performance.     

Control of SBR switching by fuzzy pattern recognition

The sequencing batch reactor (SBR) is a widely used process for biological removal of nutrients (nitrogen and phosphorus) from wastewater. It is based on the metabolism of specialised bacteria, which under alternate anaerobic/aerobic conditions uptake phosphorus and perform denitrification. Intermittent operation is normally operated on a fixed switching schedule with ample margin for possible inaccuracies, with the result that the process operation is highly inefficient. This paper proposes a switching strategy based on the indirect observation of process state through simple physico-chemical measurements and the use of an inferential engine to determine the most appropriate switching schedule. In this way the duration of each phase is limited to the time strictly necessary for the actual loading conditions. Experimental results show that the treatment cycle can be significantly shortened, with the results that more wastewater can be treated. The switching strategy is based on innovative data-processing techniques applied to simple process signals including pH, oxido-reduction potential (ORP) and dissolved oxygen (DO). They include wavelet filtering for signal denoising and fuzzy clustering for features extraction and decision-making. The formation of a knowledge-base and its adaptation during the operation are also discussed.