Luccarini L, Porrà E, Spagni A, Ratini P, Grilli S, Longhi S, Bortone G
ENEA, Wastewater Treatment and Water Cycle Unit, Bologna, Italy.
Water Sci Technol. 2002;45(4-5):101-7.
In this paper, we describe the results of research aimed to evaluate the possibility of using a neural network (NN) model for predicting biological nitrogen and phosphorus removal processes in activated sludge, utilising oxidation reduction potential (ORP) and pH as NN inputs. Based on N and P concentrations predictions obtained via the NN, a strategy for controlling sequencing batch reactors (SBRs) phases duration, optimising pollutants removal and saving energy, is proposed. The NN model allowed us to reproduce the concentration trends (change in slope, or process end), with satisfactory accuracy. The NN results were generally in good agreement with the experimental data. These results demonstrated that NN models can be used as "soft on-line sensors" for controlling biological processes in SBRs. By monitoring ORP and pH, it is possible to recognise the N and P concentrations during different SBRs phases and, consequently, to identify the end of the biological nutrient removal processes. This information can then be used to design control systems.
在本文中，我们描述了一项研究结果，该研究旨在评估使用神经网络（NN）模型预测活性污泥中生物脱氮除磷过程的可能性，利用氧化还原电位（ORP）和pH作为神经网络的输入。基于通过神经网络获得的氮和磷浓度预测结果，提出了一种控制序批式反应器（SBR）阶段持续时间、优化污染物去除和节约能源的策略。神经网络模型使我们能够以令人满意的精度重现浓度趋势（斜率变化或过程结束）。神经网络的结果总体上与实验数据吻合良好。这些结果表明，神经网络模型可作为“软在线传感器”用于控制SBR中的生物过程。通过监测ORP和pH，可以识别不同SBR阶段的氮和磷浓度，从而确定生物营养物去除过程的结束。然后，这些信息可用于设计控制系统。
