Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.

Large-scale OPF Based on Voltage Grading and Network Partition

In this paper,a model of a large-scale optimal power flow(OPF)under voltage grading and network partition and its algorithm is presented.Based on the principles of open loop operations,the node injecting current method is used to divide the large-scale power grid into voltage grading and district dividing structures.The power network is further divided into a high-voltage main network and several subnets according to voltage levels of 220 kV.The subnets are connected by means of boundary nodes,and the partition model is solved using the improved approximate Newton direction method,which achieves complete dynamic decoupling simply by exchanging boundary variables between the main network and the subnets.A largescale power grid thus is decomposed into many subnets,making the solution of the problem simpler and faster while helping to protect the information of individual subnets.The system is tested for correctness and effectiveness of the proposed model,and the results obtained are matched in real-time.Finally,the algorithm is seen to have good convergence while improving calculation speed.