Abstract

The present study focuses on examining the fate of coal constituents—carbon, sulphur, nitrogen and chlorine from coal blend to coke oven wastewater. Further, the impact of coal constituents and coke making process on wastewater quality has studied and analyzed the effects with plant data. Understanding helps in development of coke oven wastewater quality prediction model using machine learning techniques. A reliable model helps in minimize the operation costs and stable operation of treatment plant. The developed model is implemented and validated using plant scale data obtained for coke plant at Tata Steel Jamshedpur. The model provided accurate predictions of the effluent stream of by product plant, in terms of chemical oxygen demand (COD) and total dissolved solids (TDS) when using coal constituents and coke making process parameters as an input. Implementation strategy of model helps to control the wastewater quality within environmental limit with ease.

中文翻译：

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基于机器学习技术的焦化厂废水水质预测与控制

摘要

本研究的重点是研究从煤炭掺混物到炼焦炉废水中煤炭成分的命运-碳，硫，氮和氯。此外，煤成分和炼焦工艺对废水质量的影响已通过工厂数据进行了研究和分析。理解有助于使用机器学习技术开发焦炉废水质量预测模型。可靠的模型有助于最小化处理厂的运营成本和稳定运行。使用从塔塔钢铁查谢普尔（Tata Steel Jamshedpur）焦炭厂获得的工厂规模数据实施并验证了开发的模型。该模型提供了对副产品工厂废水流的准确预测，使用煤成分和炼焦工艺参数作为输入时的化学需氧量（COD）和总溶解固体（TDS）方面的数据。该模型的实施策略有助于将废水质量控制在环境极限之内。