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

Abstract

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.