At present, the conventional detection instruments in the flotation process, such as liquid level height, aeration volume, and slurry concentration, are mature and reliable, and have realized large-scale application; on this basis, the flotation process chain control process mainly realizes sequential driving and reverse sequence. Parking, equipment security alarms and protection. The basic control of the flotation process is designed and carried out around the flotation machine/column system, dosing system and pH control. Flotation machine and flotation column liquid level control usually adopts laser level gauge and float ball as liquid level height detecting instrument, cylinder and cone valve as actuator, and realizes closed-loop control through single-loop PID control algorithm or fuzzy PID control algorithm; The quantity control system adopts the thermal flowmeter as the detection mechanism and the regulating valve as the actuator, and also realizes the single-loop closed-loop control through the PID control algorithm; the flotation machine control system can also detect the temperature signal of the upper and lower bearings of the flotation machine to realize the temperature. The alarm ensures the safety of the equipment. With the increasing demand for beneficiation process, the traditional solenoid valve dosing system has gradually been unable to adapt. The closed-loop system consisting of metering pump, pulsating dosing machine, electromagnetic flowmeter and screw pump has been widely applied. Among them, the metering pump type and the pulsating type dosing machine have the characteristics of saving space and height difference, and the precision of the dosing machine; the electromagnetic flow meter and the screw pump have the characteristics of good slag resistance performance. The flotation dosing system can also achieve dosing and accumulation and report presentation under the function of the normal dosing machine. The pH control of the flotation process usually uses an industrial pH meter with flushing water as the detection mechanism, a pinch valve or a specific pneumatic cone valve as the actuator to achieve single loop control. The main progress of intelligent control of the flotation process in recent years is as follows: Based on the principle of feedforward control, the flotation liquid level model was established, and the liquid level coordinated control of the whole process of flotation was realized, which effectively suppressed the interference and shortened the fluctuation time of flotation. According to the foam image analysis of copper flotation process, the relationship between foam breaking rate and bearing capacity and tailing grade is obtained. The clustering analysis method is used to obtain expert rules, which provides guidance for the adjustment of tailings zinc grade. At the same time, the domestic production control of the flotation operation based on the foam moving speed parameter has achieved good results. Based on the results of X-ray fluorescence analysis, the domestic plant operator manually adjusts the parameters such as dosing amount and aeration amount to stabilize and improve the flotation index. However, the full-flow optimization control algorithm based on X-ray fluorescence analyzer is not yet mature. Many domestic scholars have optimized the control of flotation process, carried out research on indexing, prediction and optimization methods of flotation process, and a large number of research based on foam image processing technology, in order to provide flotation conditions and flotation process parameters for flotation operations. The correlation between the foam image and the flotation process conditions, flotation tailings grade, recovery rate, pH and other variables was studied. Research on modeling, prediction and optimization methods of flotation indicators. Wang Huiqing studied the function and structure of the flotation process optimization control, and proposed the overall control scheme of centralized management and decentralized processing problems, and determined the total objective function that maximizes the recovery rate under certain conditions of concentrate grade. A multivariate linear regression model of fine tailings grade, dosage, aeration, liquid level, ore grade and slurry concentration was established. The real-time least squares method was used to identify the dynamic model and the dynamic compensation of operating parameters. Qi Zengxian proposed a fuzzy optimization setting control method for flotation reagents consisting of a pre-set model based on case-based reasoning and a feedback compensation model based on rule-based reasoning. When the flotation-feeding boundary conditions change, the flotation dose optimization is given. The set value is to control the concentrate grade and tailings grade within the target value range. Li Qifu established a relationship model between the concentrate grade and the recovery rate and a mechanism model of the recovery rate for the flotation process. Due to the simplification and assumption of the mechanism model, the prediction results have large errors and cannot meet the industrial demand. The data modeling method of concentrate grade was discussed, and the B-spline partial least squares concentrate grade prediction model with foam characteristic data as input was established. The model improved the prediction accuracy, but its generalization ability was poor. The prediction results are unstable, and the mechanism model has better generalization ability. The information entropy method is used to integrate the mechanism model with the data model, and an integrated model of concentrate grade prediction is established, which provides a basis for establishing an optimal control system. Li Haibo uses rule reasoning and case-based reasoning techniques to establish a pre-set model of flotation process based on case-based reasoning, a soft-measurement model of concentrate grade and tailing grade based on RBF neural network, and a feedforward and feedback compensation model based on expert rules. The flotation process mixes intelligent optimization settings control methods. Liu Limin used BP neural network standard function to establish a relationship model between the recovery rate of flotation process and the five parameters of slurry concentration, pH value, aeration amount, dosage and foam layer thickness. Peng Xiuyun studied the beneficiation process of the Dashan Concentrator in Dexing Copper Mine, studied the control algorithm and structure of the multi-loop flotation liquid level system, developed the liquid level automatic control system, and proposed the optimization scheme of the control system. Sorghum used the inference control method to develop a flotation level control system and implemented the simulation in Rockwell RSLogix5000 software. In the aspect of flotation dosing control, Zhao Libing introduced a method of normalized parameter tuning method and integral separation method and setting insensitive control zone. The PID tuning of the additive added was adjusted and the algorithm was improved to improve the control performance. Lin Wei selected five parameters of saturation, gray value, energy, enthalpy value and moment of inertia. The regression model was established based on the comprehensive efficiency of flotation and the dosage of the agent. The obtained model was used to identify the flotation bubble image as PLC plus. The basis of drug control. Liu Jinping proposed a statistical pattern recognition method for the dosing health status of flotation production process with self-learning function based on the dynamic distribution characteristics of foam size. The probability density distribution of flotation bubble size was obtained by bubble image segmentation and bubble size distribution kernel density estimation. The function uses a simple Bayesian inference method to automatically identify and evaluate the health status of the flotation drug operation. Based on the feature extraction of foam image, Tang Zhaohui proposed a prediction model based on adaptive genetic hybrid neural network. Firstly, the principal component analysis (PCA) method was used to reduce the dimension of multiple extracted image features, and then adaptive genetics was used. The hybrid neural network (AGA-HNN) establishes a pH prediction model and adjusts the operating conditions in real time based on the predicted values. J. Bouchard et al. divided the control into three levels when reviewing the control of the flotation column simulator: stable flotation column input, control grade recovery, and economic indicators. C. Aldrich et al. summarized the use of foam image information for flotation control in four strategies: identifying flotation states, inferring estimated operational variables, using process control maps, and classical controls. BJ Shean et al. summarized the flotation control into four layers: instrumentation, basic control, advanced control, and optimal control. The advanced control is divided into yield cycle load control and grade recovery rate control. The optimization control is divided into two types based on model and expert system, and application cases and common method strategies are given. But the article concludes that although many improvements have been made to the basic controls since 1970, there are still few reports of successful long-running operations of fully automated and optimized flotation control systems. Daniel Hodouin's review of the controller focuses on decentralized PIDs, model-based controllers and internal model controllers. Gianni B et al. used multi-image analysis to extract the foam image structure. In addition, gray-scale co-occurrence matrix and wavelet analysis were introduced, and finally the foam-based control strategy was developed. J. Kaartinena et al. used foam image data and process data for expert system control. BK Loveday and other integrated plant measurement data modeling optimization. M. Maldonado et al. developed a rough selection control algorithm using dynamic programming. SM Vieira and other real-time fuzzy predictive control systems for the development of flotation columns. J. Jay Liu et al. proposed a method for predicting foam state control flotation using a causal model. Daniel Sbarbaro et al. proposed a monitoring system algorithm that was not based on rules and was simulated. The model predictive control of M. Maldonado and other models is explored on the optimization of the flotation column. The basic circuit of the charge control system still uses the PI controller. Olli Haavisto et al. proposed an analysis of the operator's operating level and working method based on system data, and detected and demonstrated the operating habits unique to the flotation worker through clustering and self-organizing mapping. Nunez. Felipe et al. proposed a hierarchical hybrid fuzzy control strategy, which is divided into basic level and process level, and is divided into three different working conditions according to grade and recovery rate. P. Ghobadi and other flotation process modeling uses a first-order dynamics model, the discrete distribution rate is constant, then the genetic algorithm is applied, and four basic rules are divided. OD Chuk et al. proposed a method based on predicting a given value trajectory for the basic control loop and simplified the multi-objective weighting into a single target, using a non-dominated genetic algorithm and a Pareto optimal set classification criterion. LG Bergh et al. and Danny Calisaya et al. studied the application of multivariate predictive control methods in flotation. Nakhaeie F. et al. used feedforward artificial neural networks to predict copper and molybdenum grades. The experimental model of Bergh L. et al. establishes a prediction model and combines with the expert system to control the rough selection. Sydney Mantsho, etc. can be introduced to control the concentrate grade after mintek FloatStar in mine application Sai Luoba about Silver Hill, stable flotation process, combined with the analyzer. we can supply you the Marine Engine with gearbox( Boat Engine ) ,Inboard Boat Motor, propeller and shaft , if you need any of them , please contact me. We are the only one factory that can supply you the Complete Marine Engine .
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Control and optimization of flotation process in mineral processing automation
Flotation process is to achieve valuable metal enrichment and recycling operations, the actual production process, usually concentrate grade in ensuring the premise, to maximize flotation recovery. In order to achieve the above objectives, it is necessary to detect and control the parameters such as liquid level, aeration amount, dosing amount and pH of the flotation process in real time.