Research On Optimization Decision-making Of Municipal Wastewater Treatment Plant Based On Uncertainty Theories And Methods

The planning, design and operation of municipal wastewater treatment plant is a series of decision-making processes with characteristics of multivariables and uncertain. Traditional optimization theories and methods failed to deal with intrinsic uncertainty effectively. Consequently optimization models were unilateral and limitative to a certain extent, so that it was difficult to popularize actual application. In the paper, uncartainty theories and methods were applied to optimization decision-making of municipal wastewater treatment plant, including establishment of cost function, optimal selection of wastewater treatment schemes, effect analysis of design parameter on optimal design, and solution of uncertain optimal design model, and nonlinear dynamic characteristic analysis and short-time forecast of influent flow, one of the most influencing parameter on operation of wastewater treatment plant. This research will of great significance for the design, operation and management for municipal wastrewater treatment plant.Not only the assumption of linearity for cost function but also the uncertainty resulting from collection and statistical analysis process of basic data, together with the inter-relationships among cost related factors, limited the application of traditional approaches in literatures. Considering the advantages of self-organizing, self-learning, and approximating arbitrary nonlinear continuous maps in theory, BP neural network was introduced into cost function of municipal wastewater treatment plant based on 26 data sets of Taiwan region. Design flow rate, influent BOD5 concentration, treatment degree and collection area were chosen as input variables of network, and total construction cost and plant construction cost as the output. The momentum constant and adaptive learning rate training algotithm was adopted to train network to overcome the problems of conventional BP algorithm such as falling into the local minima easily and converging slowly. Due to finite samples, leave-one-out method was applied. Early-stopping strategy was implemented to avoid over-learning. The comparative research revealed that the built BP neural network with strong learning capability outperformed multiple linear regression and can estimate cost of wastewater treatment effectively by reducing relative prediction error and improving the accuracy greatly.One important issue before design and construction of any municipal wastewater treatment plant is optimal selection of wastewater treatment schemes. Aimed at the multi-index, multi-objective and coexistence of certainty and various uncertainties involved in the optimization decision-making, this paper was intended to develop two novel approaches, that is hierarchy grey relational analysis and multi-attribute set pair analysis, to offset the shortages of conventional approaches such as considering the sole objective of minimizing system costs, and affected greatly by subjective factors, and to provide scientific basis for decision-making. The hierarchy grey relational analysis combined grey relational analysis with the idea of the hierarchy of analytic hierarchy process. It allowed for more effective reflection of actual characteristics of the problem as compared to mono level-based evaluation. In addition, the quantified evaluating scale, namely integrated grey relational grade, made wastewater treatment alternative selection more comparable and comprehensive. The multi-attribute set pair analysis developed the concept of identity - discrepancy - contrary connection degree, which should be regarded as structural function. Its unique stability analysis for basic ranking to determine other extra ranking could ensure the veracity and stability of decision results. The effectiveness of these two approaches was verified through case study. For some a municipal wastewater treatment plant, four alternatives (A2/O, triple oxidation ditch, anaerobic single oxidation ditch and SBR) were evaluated and compared against multiple economic, technical and administrative performance criteria, including capital cost, operation and maintenance cost, land area, removal of nitrogenous and phosphorous pollutants, sludge disposal effect, stability of plant operation, maturity of technology and professional skills required for operation and maintenance. The results showed that anaerobic single oxidation ditch was the optimal scheme and would obtain the maximum general benefits.Traditional optimal design of activated sludge system rarely took the parameter uncertainty into account, so that the obtained optimal solution was by no means the best for actual situation. In the paper, the typical Lawrence-McCarty mode, that is, sludge age-based design mode, was selected for optimal design of activated sludge system. Based on this, sensitivity analysis and uncertainty analysis were carried through to examine the influence of uncertain parameters on optimal design. Results indicated that influent flow and strength, parameters related with oxygen transfer efficiency, and thickening parameters of final clarifier had the most significantly influence on activated sludge system optimal design. This research was of great significance for the establishment of low-sensitivity system and the improvement of engineering practicability of theoretic optimal design solution.With the development of optimization theories during the past more than forty years, optimal design models of activated sludge system trended towards integrity. But it was difficult to obtain optimal solutions, as well as to provide dynamic modulation for design schemes. Both disadvantages weakened the practicability of optimal models. Interval optimization model can directly reflect the uncertainties that exist in actual systems, and a group of result intervals can be obtained from the solution of the model. According to personal or collective experience and prejudice, decision makers could determine detailed schemes in the result intervals combining with some other actual conditions. Obviously, interval optimization model was more scientific, applicable and operable than other optimization models. However, due to high-dimensional nonlinearity of interval optimization model, the solution process was laborious and time-consuming. In addition, the quality of ultimate solutions and calculation efficiency were sensitive to initial point for iteration. Genetic algorithm combined with classic step-search method was introduced to solve the interval optimization model for wastewater treatment plant. The verification by case study indicated that this synthetical optimization algorithm could improve searching efficiency distinctly without complicated mathematical operation such as derivative calculation. The objective, i.e. total cost, was reduced by 20 percent compared with that of classical numerical solution, which would bring great economic benefit.Influent inflow is important for the operation and control of municipal wastewater treatment plant, which is codetermined by both intrinsic factors and extrinsic stochastic factors. Its prediction accuracy not only depends on extrinsic stochastic factors, but greatly on intrinsic dynamic character. Phase space reconstruction theory was applied to calculate the largest Lyapunov exponents, based on which to recognized the chaos in influent flow time series. If there is chaos in time series, then the influent flow can be short-time forecasted. For influent flow of the No. 2 Changsha wastewater treatment plant, delay times were 2 days, and embedding windows were 9, and largest Lyapunov exponent was 0.0274. According to above results, structure of neural network was determined. The nodes of input, output and hidden layer were 9, 1, 16, respectively. This approach to determine the structure of neural network could avoid the disadvantages of dependency on experience and easy deviation. The data were divided into 36 validation data and 329 training data. Result showed that the built short-term forecasting chaos neural network for influent inflow performed well, and the largest absolute error was 0.2449 and the average prediction error was 0.077.