An ORP prediction model for acid wastewater sulfidation process based on improved extreme learning machine

Abstract

The flow of hydrogen sulfide is a crucial factor influencing the precipitation of heavy metals in acid wastewater. However, flow regulation in industrial environments often demonstrates lag. The oxidation–reduction potential (ORP) is closely linked to the flow of hydrogen sulfide. Consequently, this paper proposes an ORP prediction model that employs a double-layer improved particle swarm optimization (DLIPSO) and extreme learning machine (ELM). To overcome the limitation of particle swarm optimization (PSO) easily getting trapped in local optima, the oppositional-based learning (OBL) strategy and time-varying inertia weights are introduced to improve the search performance of the particles. Additionally, a double-layer particle swarm structure is utilized to identify the most effective combination of optimal structure and parameters for the ELM, maximizing its predictive performance. The proposed model is validated on a real dataset and compared with five other models. Experimental results indicate that the root mean square error (RMSE) of the proposed model decreased by 9.40 % to 49.76 % compared to the other models.