A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network

• The structure of RSONN can be self-organized based on the contributions of each hidden node, which uses not only the past states but also the current states.
• The appropriately adjusted learning rates of RSONN is derived based on the Lyapunov stability theorem. Moreover, the convergence of the proposed RSONN is discussed.
• An experimental hardware, including the proposed soft computing method is set up. The experimental results have confirmed that the soft computing method exhibits satisfactory predicting performance for SVI.

In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values.

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