| Urban wastewater increases rapidly along with the population growth, regional economic development, and industrialization and urbanization. More and more wastewater treatment (WWT) facilities and expects higher effluent standard are needed. Wastewater treatment is an energy intensive process. High electricity cost turns out to be a serious problem for wastewater treatment plants, which to some extent restricts the sustainable development of the wastewater treatment facilities. This dissertation mainly aims at energy efficiency improvement of WWT processes while considering the effluent standard. This dissertation is supported by Natural Science Foundation of Jiangsu Province (Grant No:BK2011346), Natural Science Foundation of Hubei Province (Grant No:2011CDB277), and the Open Research Fund of Key Laboratory of Transients in Hydraulic Machinery, Ministry of Education.An energy audit matrix for wastewater treatment processes, based on the POET (Performance, operation, equipment, and technology) energy management framework, is proposed in this dissertation, along with the corresponding methodologies and tools. In fact, the terminologies and procedures in existing energy audit guidelines are often dissimilar and sometimes overlapped. It makes the existing energy audit guidelines lack of systematicness and universality in some degree. The proposed energy audit matrix overcomes the existing energy audit guidelines with its gridding analysis method. Furthermore, the energy audit matrix is applied to a practical WWT plant with a result that the big energy saving opportunities exist in the wastewater lifting systems and the biological treatment processes. Consequently, the following sections will focus on the two key regions for energy efficiency improvement.The terminal pump station of a sewer network should be integrated with its corresponding WWT plant in energy optimization research; because the two parts are directly connected and form one continuous process. Several optimal control strategies of the terminal pump station are proposed in this dissertation. They take time-of-use electricity tariff, the number of pump switches and the accumulative operation time of individual pumps into consideration to form a multi-objective optimization problem. Finally, the optimal control strategies are applied to a terminal pump station for comparative study purpose, where two operation conditions under high to medium load and low load are investigated, respectively. The results show that the hybrid optimal control strategy of the terminal pump station has the ability to respectively save about 25% and 35% energy cost under the above two conditions. Moreover, the hybrid optimal control strategy can also reduce the number of pump switches and even the accumulative operation time of individual pumps, effectively.A LS-SVM (Least square support vector machine) based learning modelling methodology is employed to get the dynamic control models of the WWT processes. The LS-SVM based dynamic modelling method and the sparseness of LS-SVM models are intensively investigated. Specifically, the sparseness procedure of LS-SVM models is formulated as an optimal problem where the cutting rate of the training samples is taken as the optimization variable and the root mean squared error (RMSE) is taken as the objective function. Then, BSM1 (Benchmark simulation model, No 1) and a WWT plant from Zhejiang province (notated by "Z plant") are used as cases for verification study. The dynamic prediction models of the effluent indexes of BSM1 and Z plant are built for following energy optimization research.A multivariable optimal control strategy, based on the above built dynamic prediction models, for energy efficiency improvement of WWT process is proposed,,where the energy consumption is taken as the objective function and the constraints are formulated with the consideration of the effluent standards and the upper and lower bounds of the manipulate variables. A particle swarm optimization (PSO) based method is employed to solve this nonlinear optimization problem. The multivariable optimal control strategy is then respectively applied to BSM1 and Z plant, under multiple operation conditions, for verification study. In term of BSM1 under dry weather, rainy weather and storm weather, the energy efficiency optimal control strategy has the ability to obtain 2.8%,4.65% and 5.44% cost savings, respectively. Similarly, in term of Z plant under the operation conditions with high, medium and low influent COD (Chemical oxygen demand), the optimal control strategy has the potential to achieve 7.71%,9.73% and 8.9% cost savings, respectively.The dissertation has investigated the energy efficiency optimization problems of the wastewater lifting systems and the biological treatment processes. The research achievements are as follows. ① An energy audit matrix, based on POET energy management framework, is proposed for activated sludge WWT processes. The audit contents, methodologies and tools are normalized through the gridding audit matrix. ② A hybrid optimal control strategy is proposed for the terminal wastewater pumping stations. It has the ability to improve energy efficiency of the stations and meanwhile reduce the number of pump switches and even the accumulative operation time of individual pumps. ③ The inherent sparseness process of LS-SVM is formulated as an optimal problem where the cutting rate of the training samples is taken as the optimization variable. The dissertation sets up an entire research route for energy optimization of WWTs, started from energy audit to dynamic modeling, and finally to energy efficiency optimization. Moreover, some key research methodologies such as the POET based energy audit matrix, the LS-SVM based dynamic modeling methodology, and the multivariable optimal control based energy efficiency optimization are developed in the dissertation. The achievements of the dissertation improve the theoretical level of the optimal control research field of WWT; consequently, they have the ability to save energy and reduce emission as well. |
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