The Optimization Of Information And Data Driven Manufacturing Workflow

The manufacturing flow in Coal Chemical Industry has the following characters: many devices, large-scale data stream, various products, complex and long manufacturing flow. It is a typical huge system. Thus, it will be very profitable to analyze and optimize such a large system.Nowadays, because of the recession of the methanol market, many methanol manufacturers are confronted with a serious loss in methanol selling. So how to cut down the manufacturing cost is now becoming one of the most urgent issues for those manufacturers. There are many sets of equipments within the methanol manufacturing flow in one coking and chemical Company in Shanghai. In fact, the upstream device could supply to all the downstream devices according to the pipe network of the coal and chemical company. But in the current running methanol manufacturing flows of this factory, its upstream device could only supply to one specific downstream device, and one downstream device could only accept the outcome of one upstream device. It leads to an un-optimal energy consumption result. We are aiming at minimizing the energy consumtion of the manufacturing flow by effectively distributing the output of the upstream devices to the downstream devices of all streamlines. Based on the energy consumption of each device, and the related knowledge of"Input-Output Module", and the information for optimization module construction, we could find that the outcome of the upstream equipment could be rationally shared by many downstream devices and it could result in lowering the total energy consumption. So we could summarize our optimization problem like this: aimed at finding a least energy-consumption manufacturing flow, we are going to establish an optimization model, constrained by fixed manufacturing flow sequence, equipment producing capability and final methanol outcome etc, to lower the energy consumption of this company. By comparing the present status of the company and the simulation result based on the model we constructed, we find that the model could be an effective guidance to our real methanol production.In the end of the paper, I state how to combine Matlab and C# to realize the optimization model and provide an interface for the policymaker and manager and even the manufacturer. It helps to heighten the policy making level of this Coking and Chemical company.