An evolutionary robust soft measurement technique via enhanced atom search optimization and outlier robust extreme learning machine for wastewater treatment process

Accurate and prompt measurement of certain key variables occurring during wastewater treatment, especially effluent ammonia nitrogen (NH4-N) and biological oxygen demand (BOD), is of great importance. In this study, a soft measurement model combining random forest (RF), enhanced atomic search optimization (EASO) and online sequential outlier robust extreme learning machine (OSORELM) is proposed for wastewater treatment. First, RF is used to select auxiliary variables with high correlation of NH4-N and BOD as model inputs. Second, a dynamic perturbation strategy and a generalized opposition-based learning are added to ASO to improve the algorithm performance. And EASO is used for model hyperparameter optimization. Then, the optimized OSORELM model is used for soft measurements of NH4-N and BOD. Finally, to verify the stability of the model, 5 %, 10 % and 15 % noise were added for further experiments. The results show that the proposed model has better prediction and stronger robustness in soft measurements of NH4-N and BOD.

Automated summary

Accurate and prompt measurement of key variables, such as effluent ammonia nitrogen (NH4-N) and biological oxygen demand (BOD), is crucial in wastewater treatment. This study proposes a soft measurement model that combines random forest (RF), enhanced atomic search optimization (EASO), and online sequential outlier robust extreme learning machine (OSORELM) for this purpose. The model selects auxiliary variables with high correlation to NH4-N and BOD using RF, improves algorithm performance through dynamic perturbation and generalized opposition-based learning in ASO, and optimizes hyperparameters using EASO. The results demonstrate that the proposed model has better prediction accuracy and robustness in soft measurements of NH4-N and BOD.