Statistical entropy analysis of substance flows in a lead smelting process

Substance flow analysis (SFA) is of the most useful decision-support tools in environmental management, and has been applied in many studies. This research used SFA at the production-process level to realize the purposes of pollution prevention and control. One of the lead smelting processes - SKS lead smelting was chosen as our study object, and onsite monitoring data was collected from a large state-owned lead smelting enterprise in central China. All of the lead-containing material flows in the process were sampled and tested. Lead accounts of each production process and the entire system were established. Statistical entropy analysis, a method tailor-made for SFA, was applied to evaluate the SFA result of the investigated lead smelting process. Two scenarios were put forward to address the problem of mass balance failure. Scenario I represents the situation where all the lead loss is considered and is assumed to be caused by either measurement errors or uncontrolled emissions. Scenario II represents the situation of no lead loss (the ideal conditions). The results showed that lead ingot (the final product) accounted for 81.08% of the output lead, lead bullion stock accounted for 9.96%, emissions (including lead loss) accounted for 8.96%. The results also showed that the production process of Scenario II concentrated more lead than did Scenario I. Four lead-loss flows, which accounted for only 4.69% of the output substance flows, were found to determine whether the smelting process of Scenario I diluted or concentrated the lead. The uncontrolled lead-containing dust and gas emissions from the processes of casting, reverberatory furnace, and primary smelting are cause for concern. This lead loss from uncontrolled emissions and measurement errors has been long ignored, but should be given top priority for pollution prevention and the control of heavy metals in the lead smelting industry. Some recommendations for improving heavy-metal controls in production enterprises are put forward at the end of this article. (C) 2014 Elsevier B.V. All rights reserved.