Font Size: a A A

Design And Research Of Carrousel Oxidation Ditch Surface Aeration Energy-saving System

Posted on:2015-01-17Degree:MasterType:Thesis
Country:ChinaCandidate:J L LiuFull Text:PDF
GTID:2251330431954541Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
In recent years, the industrialization and urbanization of China have been developed rapidly, which brings a growing energy consumption, as well as a large number of discharge of domestic sewage and industrial wastewater. In order to cope with the increasingly prominent water crisis and energy crisis, it has been becoming more and more important to create an efficient and reliable wastewater treatment system. Modern sewage treatment process mainly has three treatment processes, and the aeration energy consumption of secondary treatment accounts for more than half of the total energy consumption of sewage treatment. By using the automatic control system to automate the aeration stage, the amount of aeration can be provided reasonably, which will reduce aeration energy consumption.Taking Shanxi Fufeng sewage treatment plant for an example, the energy-saving system of surface aeration is designed for the wastewater treatment system of Carrousel oxidation ditch. The system consists of logic control module, PC monitoring module and the wireless transmission module mainly. Among them, the logic control module can capture the sensor signals in real time and controls the field devices’ start and stop as well as the aerators’ speeds. PC remote monitoring system can monitor the status of surface aeration process in real time, which controls the aerators’ speed more effectively and achieves the role of reducing energy consumption. Wireless transmission module allows the process engineers to understand the sewage treatment situation anytime and anywhere, which can make the sewage treatment process run more efficiently and safely. The concrete contents include:Firstly, according to the frequency control theory of three-phase asynchronous motor, the overall idea is proposed to achieve the automatic control of surface aerators by using the control method of model frequency selection, and the theoretical models of automatic control are analyzed and researched thoroughly. The control mechanism for automatic shifting of aerators and the frequency-selected model is established, and the model reflects the correspondence between four main parameters (dissolved oxygen, chemical oxygen demand, mixed liquid suspended solids and the inflow of water) and aerators’ running frequency in the sewage treatment process.According to the process of Carrousel oxidation ditch, the hardware control system and network architecture for surface aeration are designed, and the model selection, assembly and communication debugging for PLC and its subsidiary intelligent modules are completed. According to the system monitoring requirements and the development ideas of monitoring software, the PC monitoring system for the surface aeration of Carrousel oxidation ditch is developed, including the monitoring software communication, the design of application interfaces and functional configuration.The procedures for the control method of model frequency selection are writed and optimized. Through the system pilot the frequency-selected model is improved and the training samples are obtained. And the training samples are used to train BP neural network and get the control model of BP neural network. The optimized frequency-selected model and BP neural network model are transformed into self-control procedures to achieve the optimization of aeration process and the precise control of process parameters.The energy-saving system of surface aeration is applied into the entire wastewater treatment system of Carrousel oxidation ditch. According to the test results, the energy-saving effect of the system is analyzed. The results show that the system can reduce aeration energy consumption effectively, and achieve the goal of energy saving.
Keywords/Search Tags:energy-saving aeration, frequency-selected model, BP neural network, PLC300, WinCC7.0
PDF Full Text Request
Related items