Hongqiu Zhu

Overlapped peaks resolution for linear sweep polarography using Gaussian-like distribution

Abstract A resolution method based on Gaussian-like distribution for overlapped linear sweep polarographic peaks was proposed to simultaneously detect the polymetallic components, such as Zn(II) and Co(II), coexisting in the leaching solution of zinc hydrometallurgy. A Gaussian-like distribution was constructed as the sub-model of overlapped peaks by analyzing the characteristics of linear sweep polarographic curve. Then, the abscissas of each peak and trough were pinpointed through multi-resolution wavelet decomposition, the curve and its derivative curves were fitted by using nonlinear weighted least squares (NWLS). Finally, overlapped peaks were resolved into independent sub-peaks based on fitted reconstruction parameters. The experimental results show that the relative error of half-wave potential pinpointed by multi-resolution wavelet decomposition is less than 1% and the accuracy of I p fitted by NWLS is higher than 96%. The proposed resolution method is effective for overlapped linear sweep polarographic peaks of Zn(II) and Co(II).

Permeability prediction model for imperial smelting furnace based on improved case-based reasoning

An intelligent prediction modeling approach integrating case based reasoning (CBR) with adaptive particle swarm optimization (PSO) is proposed for the permeability index prediction of smelting process in the imperial smelting furnace (ISF), to deal with the difficulties in describing the process with accurate mathematical models and information uncertainty. The case base is constructed directly from the production data. The cases most similar to the target case are retrieved from the case base, whose similarity measure is larger than a pre-specified threshold value. The result of the prediction model is obtained by reusing the solutions of the retrieved cases in weighted averaging. The weighted k-nearest neighbor algorithm (k-NN) is used in case retrieval, where the number of nearest neighbors and the weighted vector of features are optimized online using adaptive PSO to improve the retrieval accuracy of CBR. The experimental results of the industrial field production data show that the improved CBR model is better than the standard CBR and the model accuracy can satisfy the technological requirements.

An Intelligent Control System for Coke Calcination in Rotary Kiln

Petroleum coke is usually calcined in rotary kiln. It is a very prevalent problem of unstable production conditions and low recovery rates in coke calcination process. The temperature field in calcining zone is a crucial factor that affects the output and quality of calcined petroleum coke (CPC). Based on the analysis of the major factors affecting the temperature field in above zone, an intelligent control system for coke calcination in rotary kiln is presented to stabilize the temperature field and maximize coke calcination rotary kiln performance by expert hybrid reasoning method combining case-based reasoning with rule-based reasoning in this paper. Some main subsystems are introduced briefly. The system has been running in a coke calcination plant since 2005. Efficiency, economy, and unusual facility and reliability of control system are improved. The results of actual running show the validity and the feasibility of this system.

Optimal Control for Zinc Electrowinning Process With Current Switching

This paper is concerned with the optimal control problem for the zinc electrowinning (EW) process during the current switching period. A mathematical model is developed to reveal the dynamic characteristics of the whole plant of the zinc EW process and an energy consumption model is established to set the expected set points of the concentrations of the zinc ion and the sulfuric acid under different current. Furthermore, an optimal control problem is constructed in the light of free initial time, free terminal time, and fixed system switching time during the zinc EW process. A novel time-scaling transformation-based control parametrization method is introduced to transform the optimal control problem into a multiple parameters optimization selection problem, which can be effectively solved by the optimization algorithm. The applications on the EW process of a zinc hydrometallurgy plant demonstrate the validity of proposed method.

Intelligent operation parameters optimization of coke calcination rotary kiln

Aimed at the low quality of the product and low recovery rate of coke calcinations in rotary kiln, an intelligent optimization operations system for coke calcinations rotary kiln is presented to stabilize the temperature in calcination zone in this paper. The key operation parameters in rotary kiln are optimized using improved case-based reasoning (CBR). The case searching and matching adopted the weighted k-nearest neighbor (KNN) approach. An adaptive particle swarm optimization (PSO) with mutation is applied to optimize the weighted vector of features and the number of nearest neighbors of the weighted k-nearest neighbor approach in case searching and matching. Moreover, the control strategies in feeding rate of the disc feeder and interlock protection strategy are introduced briefly. The running results show that the intelligent optimization operation system can efficiently stabilize the temperature at calcination zone and can enhance the feeding rate control accuracy. Moreover, it can improve the coke calcination ratio and the mean productivity of the calcined coke per day. It brings significant economic benefit and social benefits to the carbon plant and have a bright future and a wide market.

Improved-PSO-Based Optimal Scheduling for Rectifier Power System

Abstract An optimization problem of series current scheduling for rectifier power system is presented in accordance with the policy of the time-of-use price. Aimed at the nonlinearity and the feature with equality and inequality constraints in this global optimization problem, an improved particle swarm optimization algorithm is proposed. In order to avoid premature convergence to local minimum, the improved particle swarm optimization algorithm adjusts its inertia weight according to the change of populations fitness, and determines its mutation probability depending on the average distance of current population. The algorithms performance is tested through three typical test function experiments. An optimal scheduling system based on the proposed methods is developed and has been put into use since Jan. 2006. Its industrial running results show its effectiveness, stability and reliability.

A Soft-Sensing Model for Water Content Based on PCA and MNN

Pneumatic drying process of ore concentrate is a typically complex industrial process, which involves the theory of gas and solid flow, heat and mass transfer and drying kinetics. There are a large number of factors affecting the pneumatic drying process. As a key parameter of the drying process, the water content of the dried ore concentrate has effect on the stable operation for the smelting process directly. The manual measurement of the water content has serious time-delay, which influences the operation optimization of the drying process. It is significant to research on the soft-sensing method for water content in drying process. The neural network model for soft sensing with many inputs and very complicated model architecture is very time-consuming to train and easy to over-fit the data with low predication accuracy and bad robustness. A soft-sensing model for water content based on PCA (principal component analysis) and multiple neural networks (MNN) is proposed to solve this problem in this paper. Firstly, PCA method was used to decrease the number of input variables, and then classified the data by k-means algorithms based on evolutionary strategies. Each kind of data after clustering is used to train a neural network sub-model. Finally, these sub-model are combined using principal components regression (PCR) method to obtain a soft-sensing model. Simulation results of the data from the practical production process show that the model can effectively sense the water content in the pneumatic drying process.