The Anaerobic/Anoxic/Oxic ($A^2$/O) process is a traditionally well-established biological wastewater treatment process (WWTP). In order to control a biological process in a laboratory environment, engineers typically adopt a methodology that relies mostly on their prior knowledge of transient and steady-state behaviors of micro-organisms. Based on this prior knowledge, our $A^2$/O process is designed to keep proper reaction time in check as well as the state defining conditions of micro-organisms. However, converse to our expectation, unforeseen experimental changes in our biological samples could cause the entire experimental process to deviate from its original course of progress. Practically, to mitigate these unexpected changes, modular neural networks (MNNs) prediction model for dissolved oxygen (DO) concentration is proposed. The MNNs consists of numerous neural network models. Each neural network model is only concerned with a single cluster. Therefore, when suitable neural network models can be selected for each condition, stable and advanced treatment performance is expected. DO concentration is an observable and controllable factor in a reactor. On the whole it affects the biological reactions and water quality. DO set-point is decided by prediction model and its control remains as an essential feature of WWTP. The predicted DO concentration is used to control the air blower, thus enabling scheduling for a stable $NH_4$-N concentration in the effluent wastewater. The proposed model has time-series static neural networks with multiple inputs and one output. The inputs - the air blower speed and DO concentration - are time-dependent variables. The output is the DO concentration in the immediate following time step. Our prediction results are compared with those of other prediction methodologies; proposed prediction model shows that it can achieve better accuracy for the DO concentration estimate than by other models. Therefore, the proposed system can be applied to a time-delayed compensation system to meet the target DO set-point tracking.