## 4 Discussion  
PID may fail to achieve the control goal or effect of the process while using the traditional PID control algorithm due to unknown and unexpected disturbances as well as significant changes in operating conditions, such as a significant change in the (i) quality of influent; (ii) weather, etc. Therefore, in general, the parameters of the traditional PID controller need to be adjusted under different operating environments. However, a long period of accumulated experience and several tests are needed in order for the traditional PID to be adjusted to achieve satisfactory results under each operating environment. Clearly, it is not feasible in real time applications and increases the difficulty in applying it in different wastewater treatment plants. Our work will reduce the difficulty of parameter tuning of the traditional PID controller, which is essential to improve the adaptability of the PID control parameters in practice.  
The simulation results in Figures 5(a), 5(b), 5(b) and 5(b) show that the DO concentration is difficult to maintain at set point under the control of the conventional incremental PID controller when the influent flow rate and quality changed greatly. On the contrary, RBFNNPID can effectively maintain the DO concentration around the set value with a relatively low error by adjusting the air flow (which can be seen in Figures 5(b), 5(b), 5(c) and 5(c)). It can be seen that the dynamic changes of the manipulated variable \(K_{L}\)AS is smooth under the control of RBFNNPID. This means we can get a more stable status by using less air supply to the aeration tank. Therefore, using RBFNNPID can reduce the aeration cost which is one of the major electrical costs of the wastewater treatment processes. It also can be verified from the results shown in Tables 1 and 2.  
According to the results of the rain weather and storm weather, which can be considered as there has the disturbances of the influent, shown in Figures 8 and 9, compared with the conventional PID controller, the RBFNNPID controller can quickly and accurately track the desired output trajectory values, which means it not only has a good tracking performance, but also has a stronger anti-disturbance ability with the changes to the set points. Figures 5(c), 5(c), 5(d) and 5(d) show the curves of the PID parameters are being adjusted adaptively. Parameters adjusted rapidly at the start of the simulation and small adjustments took place as the simulation goes on.  
Applying precise control of the concentration of dissolved oxygen can not only avoid the occurrence of sludge bulking, but also reduce the aeration energy in a wastewater treatment plant. Intermittent aeration can be successfully implemented in a small-scale wastewater treatment plant to reduce the aeration energy while ensuring good effluent water quality [38]; however the same cannot be said in a large-scale wastewater treatment plant such as Pingliang Wastewater Treatment Plant that is situated in Gansu Province, China. In a large-scale wastewater treatment plant, intermittent aeration can reduce only a fraction of aeration energy. However, continuous aeration can effectively reduce the emissions of volatile organic compounds (VOCs) from a wastewater treatment plant, which has been proven as a factor for the increase of haze in some Chinese cities [39].  
The characteristics of strong coupling, nonlinearity and large time delay of dissolved oxygen control system in activated sludge wastewater treatment and a control algorithm called RBFNNPID algorithm are discussed in this paper. However, this algorithm has not been applied directly into practice so far as it has certain complexity. We are currently undertaking the following two studies to verify its feasibility, validity, and superiority: (i) Simplifying the algorithm for practical use; (ii) Trialing the algorithm at Pingliang Wastewater Treatment Plant in Gansu Province, China.