Efficient Wastewater Treatment Optimisation with Solow-Polasky-JAYA Algorithm and Self-Organising Fuzzy Sliding Mode Control

This article introduces an innovative strategy for optimising wastewater treatment by integrating a fuzzy sliding mode controller (FSMC), self-organizing neural network (SONN) framework with the Solow-Polasky-JAYA algorithm, a novel optimisation technique. The innovation resides in the dynamic modification of SONN parameters to efficiently reduce disturbances, thus improving the efficacy of treatment. After conducting extensive experimentation, it was determined that the SoPo-JAYA optimiser exhibits rapid convergence towards optimal solutions, surpassing the performance of existing methods in terms of optimisation time. The SONN framework also works well in a lot of different operational situations when combined with parameter-controlled perturbation rejection. This makes sure that the treatment always works. The results emphasise the effectiveness of the suggested approach in attaining ideal parameter configurations and enhancing the efficiency of the process. The experimentation used MATLAB to implement algorithms and evaluate their performance. This approach used measurements from the Shafdan Wastewater Treatment Plant (WWTP) as the dataset for validation and testing in real-world scenarios. The Benchmark Simulation Model No. 1 (BSM1) framework was utilised throughout testing to provide a realistic and complete platform for evaluating the effectiveness of the suggested methodology. The method has great potential to completely transform wastewater treatment procedures and make a valuable contribution to efforts in sustainable water management.