原位污泥减量新工艺反应机制及其基于机器学习的智能控制研究

目前，我国城市污水及污泥的处理处置问题已十分突出，开发并建立强化污水处理与原位污泥减量耦合工艺，并实现在实际工程中的稳定运行是我国污水厂新建及改造的重点和难点。本研究以自主开发的一体式A2MO-M+颗粒活性炭/膜生物工艺（A2MMBR-M）为研究对象，优化系统原位污泥减量及强化污水处理的调控对策；明晰A2MMBR-M系统污泥减量的主导因素及反应机理；阐明A2MMBR-M系统有机污染物、氮、磷的降解规律及作用机制，明晰系统的分子生态学机制；揭示A2MMBR-M系统中微生物胞外聚合物（EPS）的生成代谢与污水处理过程的互作关系，建立改进的ASM2d-EPS数学模型；并进一步开展结合本体论理论和ASM2d模型的Q-learning改进算法在A2MMBR-M系统的智能化调控策略的研究，对全面实现基于机器学习的新型污水处理系统及现有污水厂在实际工程中的智能化运行具有重要的科学价值和创新意义。

At present, the problems of urban sewage treatment and excess sludge disposal in China become very prominent. Thus, the development of simultaneous enhanced sewage treatment and in-situ excess sludge reduction process, and the realization of its stable operation in an actual sewage treatment plant, become the urgent and significant issues in the construction and reconstruction in the existing sewage treatment plants. In this work, an integrated A2MO-M+GAC/MBR system (abbreviate for A2MMBR-M system) was developed and the running parameters of simultaneous enhanced sewage treatment and in-situ sludge reduction effects were optimized. The mechanism of in-situ sludge reduction and the main influencing parameters were investigated, and the transformation and mechanisms of organic matters, nitrogen and phosphorus removal in the A2MMBR-M system were further clarified. From the point of view of microbial ecology, the molecular ecological mechanisms of enhanced sewage treatment and in-situ sludge reduction effects were studied. The interaction relationships between the EPS generation and degradation and the sewage treatment process (including organic treatment and biological phosphorus removal mechanisms) in the A2MMBR-M system were investigated, and an improved ASM2d-EPS mathematical model was developed. Further researches on the smart control strategies by combining ontology theory and ASM2d mathematical model to improve the Q-learning algorithm in the A2MMBR-M system were carried out. It is of great scientific value and creative significance to realize the smart running of the existing and the new developed sewage treatment systems based on Machine Learning theory.