# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 3 Wastewater Treatment System Optimization and Control
### Process Control  
There are many factors that affect the control effect, such as the accuracy of data acquisition, data transmission speed, reference model, execution components and so on. These factors are mostly limited by equipment and models and will be studied in Sections 3.2 and 3.3. This section mainly discusses the specific control algorithms and technologies that can realize energy conservation in wastewater treatment. Compared with measurement and implementation, process control with modifying algorithms can reduce energy consumption, improve efficiency and automatic level without upgrading equipment. Table 5 shows some examples of using optimization algorithms to adjust different control processes. There are many kinds of unit-process control, such as dissolved oxygen (DO) control, solids retention time (SRT) control, return activated sludge flow control, and chemical precipitation control [112]. The process of aeration consumes the most energy of wastewater treatment, so it is appropriate to take DO control as an example [109]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processes** & **Control Object** & **Algorithms** & **Achievements** & **Reference** \\ \hline Secondary settler & Sludge height & Fuzzy algorithm & Reduce SS concentration fluctuations & [104] \\ \hline Nutrient removal & Nutrient removal & Neural networks & Remove more than 95\% of organic matters and more than 60\% of N and P & [105] \\ \hline Flows & Flows, Water levels, Overflow & Global optimal control & Decrease combined sewer overflow volumes at four overflow sites by more than 85\% & [106] \\ \hline Aeration & Dissolved oxygen & Model predictive control & Decreased the integral of absolute error with more than 40\% & [107] \\ \hline Aeration & Dissolved oxygen & Hybrid NMPC & Maintain Do level 2 mg/L, Control signals are exact and instant & [108] \\ \hline Aeration & Dissolved oxygen & Genetic algorithm & Pollution load and energy consumption savings can reach up to 10\% & [109] \\ \hline Aeration & Dissolved oxygen & Fuzzy algorithm & Save energy about 10–40\% & [110] \\ \hline Aeration & Dissolved oxygen & Fuzzy algorithm & Save about 40\% of the energy & [111] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Some examples of using optimization algorithms to adjust different control processes. High-intensity aeration is needed to maintain DO at 2-4 mg/L to maintain good microbial activity and nitrification performance in the activated sludge process. Inadequate DO will lead to the inactivity of microorganisms and affect the treatment process. However, excessive DO will deteriorate the sludge quality, makes the denitrification less efficient and leads to waste of energy [113]. DO control is realized by adjusting the air volume of the centrifuge. Proportion Integration Differentiation (PID) control, a closed-loop automatic control technology based on the feedback concept, is the main control method for DO in the past decades. However, DO control has non-linearity and time-delay, which make traditional PID control is difficult to meet the increasingly stringent control requirements [108; 109]. Some studies show that it is difficult to improve the accuracy of wastewater treatment processes control by using PI and PID [114]. Advanced control algorithms such as Model Predictive Control (MPC), Neural Network and Fuzzy Logic have gradually become the mainstream in DO control, and they have achieved some results in practical applications which are shown in table 5 [108; 109; 110; 111]. Fuzzy logic and MPC are the most commonly used advanced algorithms in DO control. MPC, a class of algorithms that using a system model to calculate a series of manipulation variables to optimize future behavior, is another method that can overcome the uncertainty and nonlinearity in this filed [107; 113]. However, it is difficult for MPC to identify many time-varying parameters to establish a reasonable prediction model.