The direction of chemical intelligent manufacturing | artificial intelligence is not reliable

In the past two years, due to the outstanding performance of artificial intelligence in some fields (not industrial fields), some people have begun to be optimistic that China is overtaking the curve of manufacturing and industrialization through "Internet +" and artificial intelligence. Can there really be such optimism?

In 2007, when I was writing my doctoral thesis, the first sentence of the first chapter of the introduction was, "In the Report of the 16th National Congress (2002), it was clearly proposed that 'industrialization is driven by informatization, and industrialization is promoted by informatization'." Later, the 17th National Congress (2007) put forward: "industrialization and information integration"; For this two, the state also set up the "Ministry of Industry and Information Technology" in 2008, indicating that the state has a deep understanding of and attention to this development strategy. Later, the 18th National Congress (2012) proposed: "the deep integration of the two". Until now, in the upsurge of intelligent manufacturing, the integration of the two is still the "foundation" of the Ministry of Industry and Information Technology. More than a decade has passed, but the two reforms are still being mentioned, which shows that this matter is not easy, and the progress is not as smooth as the Government thinks.

At present, there is a lot of talk about intelligent manufacturing in the discrete industry, but there is little talk about intelligent manufacturing in the chemical industry. So, chemical intelligent manufacturing, in which direction is it developing?

Chemicals are already on the automation fast track

The chemical industry has long realized the primary intelligent system - automated control. Due to the continuity of the chemical process and the large-scale of the device, and the huge investment in the device (billions of dollars of investment), the chemical industry (including oil refining, petrochemical) has long put forward very high requirements for process automation, and began to use DCS for process control in the 1970s. Automation improves the stability and safety of chemical production, and it is also easy to increase the profit margin of the factory (increasing profits is the direct motivation for enterprises to adopt new technologies in the market economy environment). The current level of technology can make more than 80% of the production workshops and operations of chemical production unmanned, mainly in some of the processing and transportation of solids to achieve automation is more difficult. Large chemical equipment production workshop unmanned is a normal phenomenon, relying on pumps, compressors to achieve the flow of materials in the closed pipeline system, relying on a variety of temperature, pressure, liquid level, flow control to achieve the automatic operation of matter and energy in each operating unit.

Technically feasible or optimal, does not mean the best economic benefits. In particular, some small devices, completely using automatic control system system unit cost is high; When labor costs are low, manual operation is preferred. Therefore, the automation rate of the chemical industry in the real world is determined by the technical level and economic benefit (investment cost, labor cost).

Traditional AI is not suitable for the chemical industry

The core of traditional artificial intelligence (big data, machine learning) is to summarize and extract rules from historical data, so as to predict the future. Its theoretical basis is that the running data contains all the important hidden information of the system, and the rules and knowledge of the system can be mined directly from the data without studying the mechanism of the problem.

This kind of artificial intelligence is not suitable for the chemical industry, and the intelligent production of the chemical industry is extremely limited. For three reasons:

1. The operation mechanism and mathematical model of chemical plant are relatively complete. Chemical engineering, as an engineering discipline with more than 100 years of development, has a relatively complete knowledge system. Chemical plant as a manual design system, the designer has been clear about the inherent characteristics and mechanism of the device when designing, and has known the mathematical model of the device. So there is no need to use artificial intelligence to mine and discover knowledge. Even when the mechanism is unclear or the boundary is uncertain, some conventional and traditional data analysis methods are sufficient to deal with the problems in chemical industry.

2. As a strictly controlled system, chemical equipment has a lot of data but monotonous, and the information is too low to mine knowledge. Because the chemical process is strictly controlled by various control systems and the production is stable, the data generated is a lot but the distribution is narrow, and it is impossible to use artificial intelligence to extract rules or knowledge from this big data with little information. 100 or 10,000 identical pieces of data contain the same amount of information as one piece of data.