Dual Rule-Based Weighted Fuzzy Interpolative Reasoning Module and Temporal Encoder-Decoder Bayesian Network for Reagent Addition Control

Reagent addition control level of the zinc first rougher has a significant impact on quality of the zinc concentrate. As froth visual features are important indicators of working states in froth flotation, they are usually controlled to optimal setpoints by reagent addition for the desired concentrate. Existing methods usually construct a rule base for feature setpoint generation and design an error-driven feedback controller for reagent addition. However, there are still some issues: 1) a portion of data is used for rule base establishment and knowledge in remaining data is ignored; 2) as the rule base is sparse, it may lead to match failures caused by insufficient knowledge; 3) existing controllers only consider the error at the current time, while ignore the temporal information in error sequences. Therefore, we propose a reagent addition control strategy with the dual rule-based weighted fuzzy interpolative reasoning module and temporal encoder-decoder Bayesian network. Firstly, we design a dual rule base with complementary data to fully mine knowledge. Then, we explore a weighted fuzzy interpolative reasoning module for feature setpoint generation. This module selects several nearest neighbor rules in a positive rule base as interpolative candidates and determines rule weights via the nearest neighbor rules in the negative rule base. After that, we introduce a temporal encoder-decoder network by taking temporal error sequences as the input to recognize working states, and use a Bayesian network to realize reagent adjustment. Ablations and comparative experiments on industrial data show the effectiveness of the proposed control strategy.