An anaerobic fermentation process for biogas production integrated with wastewater purification in a modern wastewater treatment plant (WWTP) of designed nominal capacity 27,000 m³/day was modelled using artificial neural networks (ANNs). Neural models were trained, validated, and tested based on real-scale industrial data (covering three years of continuous plant operation), considering both technological aspects of the process and treated wastewater quality. An innovative approach addressing the simultaneous effect of seven adjustable main plant operation parameters together with wastewater characteristics (five parameters) on biogas production is reported for the first time in the literature. A parameter sensitivity analysis indicated clearly the higher importance of the operation process parameters on the biogas yield compared to the wastewater quality (COD, BOD₅, TSS, P_g, N_g). The operation process parameters were the subject of modelling and analysis in respect to new, innovative possibilities, and technological strategies for biogas yield enhancement. The ANN model presented can be used as a predictive tool, an important element in such complex processes as steering/control strategies or for their optimisation procedures, as well as in the testing of other promising process intensification and optimisation scenarios.

设计产能为27,000 m ³的现代化废水处理厂（WWTP）中的沼气生产厌氧发酵工艺与废水净化集成/ day是使用人工神经网络（ANN）建模的。基于实际的工业数据（涵盖了工厂连续三年运行）对神经网络模型进行了训练，验证和测试，同时考虑了过程的技术方面和处理后的废水质量。文献中首次报道了一种创新方法，该方法解决了七个可调节的主要工厂运行参数以及废水特征（五个参数）对沼气生产的同时影响。参数敏感性分析清楚地表明，与废水质量（COD，BOD ₅，TSS，P _g，N _g）相比，操作过程参数对沼气产量的重要性更高。）。关于新的，创新的可能性和沼气产量提高的技术策略，操作过程参数是建模和分析的主题。所提出的ANN模型可以用作预测工具，在诸如转向/控制策略之类的复杂过程中或在其优化过程中以及在其他有前途的过程强化和优化方案的测试中都非常重要。