Energy Optimization On Oxygen Supply System Of Biological Oxidation Pretreatment Process

Biological oxidation pretreatment process is the main gold extraction technology to dispose refractory gold ore with high arsenic and sulfur, which can improve the yield of gold. Air input of each oxidation tank influences oxidation efficiency between pulp and bacteria, even to the yield of gold. While oxygen supply system is one of the main energy consumption process. It is of great significance to decrease cost and energy consumption for the process of biological oxidation pretreatment by optimization control technology without impacting the product quality.For the current biological oxidation pretreatment process, the control of air input has still been adjusted manually with experience. Moreover, for the complicated ingredients of oxidation tank, pretreatment is strong nonlinearity, coupling and large time delay, which can not realize real time control for air input. And in most cases, air input of each oxidation tank is oversupply rather than less. Thus, it may lead to high dissolved oxygen levels and low oxygen utilized ratio so that to decrease energy utilization rate largely. The object of this paper is oxygen supply system of Cyanide leaching gold biological oxidation pretreatment process. Oxygen supply prediction of biological oxidation tank and the optimization methods of energy efficiency are studied with the actual production situation of the pretreatment. The detailed content is arranged as follows.1) An intelligent integrated model based on optimum weight is proposed to predict air input of oxidation tank online. The proposed model contains two parts: one is oxygen consumption mechanism model of biological pretreatment process via analyzing mechanism of the process; another part is air input prediction model based on online support vector regression according to correlation analysis on collected data from oxygen tank. And the intelligent integrated model is built by using weighted summation of two prediction models. The weight coefficient can be solved by optimal weighting method. Using the real data collected from the pretreatment process, the experimental results show that the prediction accuracy of intelligent integrated model is better than any single prediction model. The proposed model can predict air input of each oxidation tank efficiently. Thus, the prediction result provides guidance for the production.2) To make oxygen supply system achieve the optimal operating condition of energy efficiency and improve energy utilization rate, it is necessary to analyze and evaluate energy saving of oxygen supply system. On this basis, effective energy saving measures can be proposed for the current production plant. According to the online air input prediction of biological oxidation pretreatment process, air input of each oxidation tank is controlled real- time and automatically. Thus, it can save energy and reduce consumption.