At present, the construction of digital factories has become a major trend in the development of the petroleum and chemical industries. How to design a digital factory system that is safe, efficient, and human-friendly in light of the pain points in the chemical production process in China, and gradually realize smart production? On April 19th, at the 2018 Chemical Industry Digital Factory Construction Seminar held by Shanghai Unison Tongzhi Information Technology Co., Ltd. in Nanjing, Jiangsu, industry experts focused on chemical production management and jointly discussed how to establish digitalization that meets the characteristics of China's chemical industry production. Factory system.
Chen Tingxuan, executive deputy secretary-general of the Shanghai Intelligent Manufacturing Industry Technology Innovation Strategic Alliance, said that the intelligentization of the manufacturing process is the direction of manufacturing development and the development process of intelligent manufacturing. In the age of the Internet of Things, digitalization, networking, and intelligence are the main lines for smart manufacturing, and digitization is the primary issue that companies need to solve. It is also a prerequisite for networking and intelligence.
“The oil and chemical industry is in the midst of changes brought about by digital and intelligent technologies. Digitization and informationization are the only ways to realize intelligence.” Zhou Zhijie, chairman of the company, stressed that in the process of digital plant construction, however, There are problems such as large differences in personnel quality, high mobility of employees, difficulty in accumulation of experience, islands of information, difficulty in landing the system, etc., and the chemical industry has a complex process with high coupling. The process is toxic, flammable, explosive, and lacks an accurate model. Human intervention and prejudgment. Building digital factories can achieve safer and more efficient goals and help companies continue to progress.
Lu Zhenhua, deputy general manager of Nanjing Chengzhi Clean Energy Co., Ltd.'s Nanjing plant, told reporters that digital factory construction is the development direction of the industry. Digital factories can help businesses achieve their goals of reducing repetitive operations, strengthening process specifications, using data effectively, and improving collaboration efficiency. Following the application of the digital factory system by Nanjing Chengzhi, 20 on-line functions have been put in place and 87 requirements have been met, greatly improving the efficiency and quality of vehicle management, equipment management and personnel management.
"In the next 10 years, 12 technologies such as mobile Internet, cloud computing, Internet of Things, automation of knowledge, high-end robots, 3D printing, advanced oil and gas exploration and recovery technologies, and advanced materials will have an important impact on manufacturing. Unleashing the potential of data, rather than simply collecting it and building an effective model, can really solve the industry's pain points.” Zong Wei, business development manager of the Shanghai branch of Cisco Systems (China) Network Technology Co., Ltd., told reporters.
According to relevant statistics, reducing the system's pause time, reducing the rate of defective products, optimizing and improving inventory, promoting new products, and reducing energy consumption are issues that companies are concerned about. How to use the new technology of the Internet of Things to collect, filter, and effectively use information will be an important means to solve the above problems.
Zong Jun believes that in order to tap the potential of data, it is necessary to obtain data from the equipment, transfer the data to the right place, and preprocess and convert the data. Based on the guarantee of data reliability and security, according to user needs, Complete the massive data processing process. According to reports, in terms of digital refinery practices, Cisco has achieved wireless location tracking, real-time gas monitoring and detection, process condition optimization and predictive maintenance, real-time tracking of all plant valve status, real-time edge processing of key data, and more. Companies save a lot of operating costs and increase production efficiency.
Zhou Zhijie suggested that digital plant construction should be divided into three steps. The first is to make up for shortcomings, consolidate basic management, improve data collection capabilities, informatization, and digital capabilities. The second is to realize all-digital business processes, build data centers, and release data values. The third is to innovate business models, establish a sustainable and viable ecosystem, and promote digital transformation. In addition, efforts must also be made to address the issues of standardization and intelligence, and the technical layout must be forward-looking. Otherwise, it may lead to frequent changes or even come back and increase the burden on enterprises.
Lu Zhenhua said that in the production process of some chemical companies, many work performed at the site still requires secondary recording or registration in the office, and the process and specification lack enforcement methods in the implementation process. Large amounts of data are difficult to manage, and the workload for statistical searches is huge, making it difficult to realize the true value of the data. Many tasks at the site require repeated communication and handover, which is time consuming, labor-intensive, and error-prone.
A production supervisor of AkzoNobel Functional Coatings Co., Ltd. said that when customers consult the production status of orders, the departments involved in order production cannot be timely due to the fact that the production process is difficult to trace back, information is not delivered in a timely manner, and statistical data is time-consuming and labor-intensive. Accurately respond to related issues and generate huge communication costs.
“At present, the problems of on-site coordination, difficulty in information communication, difficulty in personnel and equipment management, difficulty in standard implementation, poor training results, multiple process changes, miscellaneous equipment types, and difficult intelligent transformation are widely encountered in the production management of industrial enterprises. Gong Tongzhi has developed intelligent systems such as digital plant management and control, smart work orders, pipeline flow rates, mixed coal calculations, and unit conversions. It has realized the information platform, operation flow, data standardization, performance visualization, experience data, and scientific decision-making. To help companies significantly improve the level of safety management, personnel efficiency, equipment operation rate and process stability, etc.. Zhou Zhijie told reporters.
According to reports, during the overhaul of an enterprise industrial park in Yantai, on-site management was cumbersome, and it was difficult to standardize contractor management. After the introduction of the KIC system in the gasification plant, the management of the contractor has become faster and more elaborate. It has provided reliable data link support for the prevention and analysis of accidents. The contractor’s illegal activities have been greatly reduced; the job sheet has been realized. The risk management in the mobile visualization management and operation process has greatly reduced the accident rate.
A technician from Yantai Wanhua Chemical told the reporter that after the application of the Mobility Control System of the same company, the formation of big data provided a reliable data link for the prevention and analysis of accidents, and realized the management of personnel behavior safety. Diversified and personalized training materials and testing materials are targeted and accurately pushed. The mobile training mode enables personnel to learn anytime, anywhere, and improves the flexibility of training. The security job ticket function provides more risk assessment time before the job. It facilitates the archiving of job orders, real-time query and statistical analysis, and effectively manages and controls non-operational risks in the process.