A Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Prediction

This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features of the data collected from multiple sensors in the thickener to predict underflow concentration. The concentration is the key factor for future mining process. This model includes encoder and decoder. Their function is to capture spatial and temporal importance separately from input data, and output more accurate prediction. We also consider the domain knowledge in modeling process. Several supplementary constructed features are examined to enhance the final prediction accuracy in addition to the raw data from sensors. To test the feasibility and efficiency of this method, we select an industrial case based on Industrial Internet of Things (IIoT). This Tailings Thickener is from FLSmidth with multiple sensors. The comparative results support this method has favorable prediction accuracy, which is more than 10% lower than other time series prediction models in some common error indices. We also try to interpret our method with additional ablation experiments for different features and attention mechanisms. By employing mean absolute error index to evaluate the models, experimental result reports that enhanced features and dual-attention modules reduce error of fitting ~5% and ~11%, respectively

A Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Prediction

This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features of the data collected from multiple sensors in the thickener to predict underflow concentration. The concentration is the key factor for future mining process. This model includes encoder and decoder. Their function is to capture spatial and temporal importance separately from input data, and output more accurate prediction. We also consider the domain knowledge in modeling process. Several supplementary constructed features are examined to enhance the final prediction accuracy in addition to the raw data from sensors. To test the feasibility and efficiency of this method, we select an industrial case based on Industrial Internet of Things (IIoT). This Tailings Thickener is from FLSmidth with multiple sensors. The comparative results support this method has favorable prediction accuracy, which is more than 10% lower than other time series prediction models in some common error indices. We also try to interpret our method with additional ablation experiments for different features and attention mechanisms. By employing mean absolute error index to evaluate the models, experimental result reports that enhanced features and dual-attention modules reduce error of fitting ~5% and ~11%, respectively

Sewage sludge heavy metal analysis and agricultural prospects for Fiji

Insoluble residues produced in Waste Water Treatment Plants (WWTP) as by products are known as sewage sludge (SS). Land application of SS, particularly in agricultural lands, is becoming an alternative disposal method in Fiji. However, currently there is no legislative framework governing its use. SS together with its high nutrient and organic matter contents, constitutes some undesired pollutants such as heavy metals, which may limit its extensive use. The focus of this study therefore was to determine the total concentrations of Pb, Zn, Cd, Cu, Cr, Ni and Mn in the SS produced at the Kinoya WWTP (Fiji) and in the non-fertile soil amended with the SS at 20, 40, 60, 80% application rates and in the control (100% Soil). The bioavailable heavy metals were also determined as it depicts the true extent of metal contamination. The treatment mixtures were then used to cultivate cabbage plants in which the total heavy metal uptake was investigated. Total Zn (695.6 mg/kg) was present in the highest amounts in the 100% SS (control), followed by Pb (370.9 mg/kg), Mn (35.0 mg/kg), Cu (65.5 mg/kg), Cr (20.5 mg/kg) and finally Cd (13.5 mg/kg) and hence a similar trend was seen in all treatment mixtures. The potential mobility of sludgeborne heavy metals can be classified as Ni > Cu > Cd > Zn > Mn > Cr > Pb. Total metal uptake in plant leaves and stems showed only the bioavailable metals Cu, Cd, Zn and Mn, with maximum uptake occurring in the leaves. Ni, despite being highly mobile was not detected, due to minute concentrations in the SS treatments. Optimum growth occurred in the 20 and 40% SS treatments. However maximum Cu and Mn uptake occurred in the 40% SS treatment thereby making the 20% treatment the most feasible. Furthermore the total and bioavailable metal concentrations observed were within the safe and permitted limits of the EEC and USEPA legislations