Water quality index is the most convenient way of communicating water quality status of water bodies, but its evaluation requires subjectivity in terms of user involvement and dealing with uncertainty. Recently, artificial intelligence algorithms that are appropriate for nonlinear forecasting and also dealing with uncertainties have been applied to various domains of water quality forecasting. This paper focuses on development of a data-driven adaptive neurofuzzy system for the water quality index using a real data set obtained from eight different monitoring stations across River Satluj in northern India. Novelty in the paper lies in the estimation of water quality index using two different clustering techniques: fuzzy C-means and subtractive clustering-based ANFIS and assessing their predictive accuracy. Each model was used to train, validate, and test the index that was obtained from seven water quality parameters including pH, conductivity, chlorides, nitrates, ammonia, and fecal coliforms. The models were evaluated on the basis of statistical performance criteria. Based on the evaluations, it was found that the SC-ANFIS method gave more accurate result as compared to the FCM-ANFIS. The tested model, SC-ANFIS model, was further used to identify those sensitive parameters across various monitoring stations that were capable of causing change in the existing water quality index value.

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All the industrial process applications require solutions of a specific chemical strength of the chemicals or fluids considered for analysis. Such specific concentrations are achieved by mixing a full strength solution with water in the desired proportions. In this paper the control the concentration of one chemical with the help of other has been analyzed. This paper features the influence of different controllers like P, PI, PID and Fuzzy logic controller upon the process model. Model design and simulation are done in MATLAB SIMULINK, using fuzzy logic toolbox. The concentration control is found better controlled with the addition of fuzzy logic controller instead of PID controller solely. The improvement of the process has been observed.