Optimal on-off level control design by electrical analogy for improved moving fine mesh filter system performance in wastewater treatment plants

Abstract

The operation of Wastewater Treatment Plants (WWTPs) has several stages. Physical treatment which is the first stage of wastewater treatment, depends not only on the influent physical characteristics, but also on the design of system mechanics such as the moving fine mesh filter system structure. The crucial parameters of the filtering system are particle size distribution and floc strength. However, clogging of filter media is described as a difficulty in biological treatment, while development of an optimized control in moving fine mesh filter system turns the difficulty into advantage on both energy consumption and filtration. This paper is an attempt to present the electrical analogy of the moving fine mesh filter systems demonstrated deterministically based on the fluid dynamics principles and propose a level controlled design solution for the system operation with optimal on-off cycles. Electrical analogy of water-tree model is used to find the optimal frequency of on-off cycles in the level controlled moving fine mesh filter system. Obtained system design is tested via computer simulations to reveal efficacy of the method and the results of optimal solution in design present a useful alternative with good accuracy in the moving fine mesh filter systems. To achieve the optimal operating level of the moving fine mesh filter system, a model-based optimal control problem is defined and the problem is combined with modified parameters. The optimal level controlled design shows better moving fine mesh filter system performance compared to the conventional approach. The level control approach has been shown to significantly improve the duration and accuracy of the moving fine mesh filter system variation in design. The proposed system is also tested on the Tatvan WWTP as a case study to demonstrate the practical value of proposed methods. The simulation results are compared with the actual data collected throughout the year and filtering performance is observed to improve significantly in both winter and summer conditions. Obtained results clearly reveal the superiority and practical value of the proposed optimal design over existing models.