基于遗传算法的污水处理工艺流程优化

污水处理工艺是针对城市生活污水和工业废水的处理问题所提出的一套完整的解决方案,并且被广泛应用于各个领域.城市污水处理工艺应根据处理规模、水质特性、受纳水体的环境功能及当地的实际情况和要求,在工艺技术特性与经济成本的衡量中选择优化方案确定,其可以看作是一种特殊形式的多参数优化问题:首先统计污水处理的工艺方法并设计污水处理工艺知识库;其次将各工艺的参数和环境信息作为输入,以工艺知识库作为支撑,依托设定好的智能算法自动生成由工艺知识库中的工艺组合而成的方案,方案包括各工艺模块的顺序、工艺内部组件的尺寸、预测运行成本以及处理效果.本文提出了使用messy遗传算法对污水处理工艺流程进行选优推荐,将工艺序列的总成本的倒数作为适应度,根据工艺知识库与优化目标,自动生成在多个污染物指标达标的情况下成本最低的优化方案.通过实验验证,messy遗传算法能够在工艺序列长度变化的情况下实现方案的高效准确推荐.

Optimization of Sewage Treatment Processes Based on Genetic Algorithm

Sewage treatment processes are a set of complete solutions for the treatment of urban domestic sewage and industrial wastewater, and they are widely used in various fields. According to the treatment scale, the characteristics of water quality, the environmental functions of the receiving water, and the actual situation and requirements of the local area, the treatment processes of urban domestic sewage should be optimized and determined after the measurement of technological characteristics and economic costs. Thus, the design of urban domestic sewage treatment processes can be regarded as a special form of multi-parameter optimization. Firstly, the process methods of sewage treatment should be sorted out, and the knowledge base of sewage treatment processes should be designed. Secondly, with the parameters and environmental information of each process as inputs, a scheme composed of the processes from the knowledge base is automatically generated based on the sewage treatment process database and the set intelligent algorithm. Specifically, the scheme includes the sequence of each process module, the size of internal components, the operation cost prediction, and the treatment effect. This study adopts the messy genetic algorithm to recommend the sewage treatment processes. The multiplicative inverse of the total cost of the process sequence is taken as the fitness, and the optimized scheme with the lowest cost is automatically generated when multiple pollutant indexes reach the standard. Experimental results show that the messy genetic algorithm can efficiently and accurately recommend the scheme when the process sequence length changes.