The shortage of computation methods and storage devices has largely limited the development of multi-objective optimization in industrial processes. To improve the operational levels of the process industries, we propose a multi-objective optimization framework based on cloud services and a cloud distribution system. Real-time data from manufacturing procedures are first temporarily stored in a local database, and then transferred to the relational database in the cloud. Next, a distribution system with elastic compute power is set up for the optimization framework. Finally, a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process. With the application of this optimization service in a cloud factory, iron production was found to increase by 83.91 t∙d⁻¹, the coke ratio decreased 13.50 kg∙t⁻¹, and the silicon content decreased by an average of 0.047%.

计算方法和存储设备的短缺在很大程度上限制了工业过程中多目标优化的发展。为了提高过程工业的运营水平，我们提出了基于云服务和云分配系统的多目标优化框架。制造过程的实时数据首先临时存储在本地数据库中，然后传输到云端的关系数据库中。接下来，为优化框架设置具有弹性计算能力的分配系统。最后，提出了一种基于深度学习和进化算法的多目标优化模型来优化高炉炼铁过程中几个相互冲突的目标。随着这个优化服务在云工厂的应用，^-1，焦比降低13.50 kg∙t ^-1，硅含量平均降低0.047%。