How to view the field of intelligent chemical industry?

Although the production equipment control technology of the chemical industry as a whole is significantly different from that of the discrete manufacturing industry, the chemical industry and the discrete manufacturing industry are basically the same from the perspective of the overall digital solution structure of the enterprise. It is currently based on the Purdue Enterprise Reference Architecture (PERA) from the 1990s, as shown in the image above. At the top of this structure is the Enterprise Resources Planning (ERP) layer, which covers all aspects of a company's management and operations: sales, purchasing, production, distribution, human resources, customer service, corporate finance, and so on. The second layer is the Manufacturing Execution System (MES), which is responsible for arranging and coordinating the production equipment of the enterprise to execute and complete the order according to the order instructions issued by the ERP layer. It needs to be emphasized here that compared with discrete manufacturing, the biggest pain point of the traditional PERA architecture in the application scenario of process manufacturing, especially the chemical industry, occurs in this second layer MES. First of all, the key to MES ensuring the timely completion of target orders lies in the accurate estimation of a key parameter, which is called Overall Equipment Effectiveness (OEE), which is a percentage and the product of three percentage indicators. 

The three percentage indicators are: 

1. availability rate is used to measure the operating rate of equipment (planned time minus downtime, divided by planned time, multiplied by percentage). 

2. performance rate is used to measure the efficiency of the equipment (the ratio of the operating speed to the standard operating speed, representing the loss on the work) 

3. Quality Rate Measures the percentage of qualified products (the proportion of qualified products to total production)

In the discrete manufacturing industry with relatively strong OEE parameters estimated by MES, the competitive competitors with faster, more accurate, more flexible and more intensive operating performance have become the model template for peers to follow and chase, and in the chemical industry with generally weak OEE parameter estimation ability of MES, the competitive mentality of the players competing on the track is more subtle: Strategically, they play chess around raw materials and energy, but at the tactical level of production and operation, they are more like making sure that they "drive carefully for thousands of years" and then hoping that each other will retire from the game without a fight. So the chemical industry is not so much competing as heavier than rotten ingredients. Then the question comes, compared with discrete manufacturing, why MES in the chemical industry can be so weak to predict OEE parameters, so that there is often so much uncertainty in the delivery of orders on time? The main problem is that the latter three layers of PERA are not as digitalized as discrete manufacturing. 

These last three layers are the Supervisory Control and Data Acquisition (SCADA) layer, the control system layer dominated by DCS/ PLCS, and the field production equipment layer connected to various sensors. In simple terms, Layer 3 SCADA in the chemical industry monitors abnormal fault alerts based on real-time data generated by the production line, but does not have predictive maintenance functions. This function is the key function of the MES layer to accurately predict OEE parameters, which can be detected in advance according to daily data 1-2 months before serious abnormalities occur in the aging production equipment and notify the ERP layer to arrange the purchase of new equipment and parts and uninterrupted replacement, so as to avoid the unplanned parking and maintenance major surgery in the above two news. The second layer of control system is mainly based on PID controller, which has no ability of model predictive control (MPC) technology to prospectively control the dynamic behavior of the process from the internal mechanism. At present, most discrete manufacturing industries (such as automobiles and home appliances) can realize the automatic replacement of MPC, while the MPC application rate of chemical industry is still not high, the most simple and intuitive explanation is: using a mathematical model to predict the appearance of a basin of water poured out is far more difficult than predicting the appearance of billiard balls hitting each other. To maximize the value of predictive maintenance and model predictive control in the chemical industry, the key is to be able to create high-precision digital twins for chemical production lines, as in the discrete manufacturing industry, and cloud computing, edge computing and 5G signal technology to measure, transmit and process data in large throughput as the infrastructure to "feed" the digital twin in real time. Although the world is still in the exploratory stage for the commercialization of smart chemical industry, I think the biggest gap between China and Europe and the United States is the chemical digital twin model, rather than cloud computing, edge computing and 5G technology.