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The tailings grade is a crucial indicator for controlling and optimizing mineral flotation processes. However, detecting/predicting tailings grade online faces challenges due to intricate physicochemical reactions in three-phase slurries with the strongly-coupled characteristics of the technical process. This paper introduces a novel multi-source features-fused two-stage tailings grade prediction model called MsFfTsGP by integrating multiple-source froth image sequences (FISs) with available traditional technical variables (TTVs, e.g., the reagent dosage and measured grades) from multiple flotation cells, mimicking the on-site two-stage prediction mode of experienced operators. Namely, an initial evaluation is made based on the source-ore characteristics using the FISs of the first rougher, while the second stage provides a sophisticated prediction of the tailing grade by considering both the source-ore characteristics and the flotation behavior of the whole circuit process. Specifically, FISs captured from the first rougher are fed into a multi-stream 3D convolutional network (Ms3DCN) for making an initial prediction. Then, the initial prediction result is combined with the FIS information from the last scavenger (also processed by the Ms3DCN) and available TTVs based on the attention mechanism to make a sophisticated prediction. Extensive experiments on a real industrial lead-zinc flotation process demonstrated a significant improvement of the proposed model in prediction accuracies. Integrating multisource FISs with TTVs from multiple coupled flotation cells shows great potential for achieving accurate tailings grade prediction, making MsFfTsGP a promising tool for enhancing the control and optimization of flotation processes. [ABSTRACT FROM AUTHOR]

The quality and ratio of clinker, the fineness of cement are key factors affecting the strength of cement. In order to realize the target tracking control of cement strength, a self-learning fuzzy predictive control algorithm is proposed to calculate the adjustment variables of cement grinding process Considering the serious hysteresis of cement strength detection, the 3-day cement strength needs to be predicted, statistical models cannot actually guide the production for the loss of working conditions. In this paper, a 3-day strength prediction model is established based on step response which is trained by difference data. The cement grinding process is a complex industrial process which is composed of multiple sub-processes and the variables are coupled each other, according to the characteristic, a multi-variable distributed fuzzy control algorithm (MVDFC) is proposed, and the fuzzy rules are mined by Apriori algorithm. The verification results from the actual field data demonstrate the effectiveness and superiority of the proposed method, which can completely meet the actual needs of the process. • A Self-Learning Fuzzy Predictive Control Algorithm for Calculation of the Adjustment Variables of Cement Grinding Process. • Creating a 3-day cement strength prediction model based on step response and training it by difference data. • Proposing a multi-variable distributed fuzzy control algorithm and extracting fuzzy rules by Apriori algorithm. [ABSTRACT FROM AUTHOR]