The Chemical Manufacturing subsector is based on the transformation of organic and inorganic raw materials by a chemical process and the formulation of products. This subsector distinguishes the production of basic chemicals that comprise the first industry group from the production of intermediate and end products produced by further processing of basic chemicals that make up the remaining industry groups.
In a World First, Yokogawa and JSR Use AI to Autonomously Control a Chemical Plant for 35 Consecutive Days
Yokogawa Electric Corporation (TOKYO: 6841) and JSR Corporation (JSR, TOKYO: 4185) announce the successful conclusion of a field test in which AI was used to autonomously run a chemical plant for 35 days, a world first. This test confirmed that reinforcement learning AI can be safely applied in an actual plant, and demonstrated that this technology can control operations that have been beyond the capabilities of existing control methods (PID control/APC) and have up to now necessitated the manual operation of control valves based on the judgements of plant personnel. The initiative described here was selected for the 2020 Projects for the Promotion of Advanced Industrial Safety subsidy program of the Japanese Ministry of Economy, Trade and Industry.
The AI used in this control experiment, the Factorial Kernel Dynamic Policy Programming (FKDPP) protocol, was jointly developed by Yokogawa and the Nara Institute of Science and Technology (NAIST) in 2018, and was recognized at an IEEE International Conference on Automation Science and Engineering as being the first reinforcement learning-based AI in the world that can be utilized in plant management.
Given the numerous complex physical and chemical phenomena that impact operations in actual plants, there are still many situations where veteran operators must step in and exercise control. Even when operations are automated using PID control and APC, highly-experienced operators have to halt automated control and change configuration and output values when, for example, a sudden change occurs in atmospheric temperature due to rainfall or some other weather event. This is a common issue at many companies’ plants. Regarding the transition to industrial autonomy, a very significant challenge has been instituting autonomous control in situations where until now manual intervention has been essential, and doing so with as little effort as possible while also ensuring a high level of safety. The results of this test suggest that this collaboration between Yokogawa and JSR has opened a path forward in resolving this longstanding issue.
Make Digital Twins an Integral Part of Your Sustainability Program
Digital solutions provide the visibility, analysis and insight needed to address the challenges inherent in sustainability goals. A digital twin strategy as part of an overall digitalization plan can be a crucial capability for asset intensive industries such as refining and chemicals. A digital twin needs to encompass the entire asset lifecycle and value chain from design and operations through maintenance and strategic business planning.
Comprehensive sustainability solutions are stretching the capabilities of thermodynamic first principle-based digital twins and driving the need for the next generation of solutions. Reduced order hybrid models offer a critical capability to achieve digitalization, sustainability and business goals faster. Reduced-order models can abstract models to enterprise views which inform executive awareness and strategic decision-making. Site-wide models can run faster and more intuitively to drive agile decision-making and optimize assets to achieve safety, sustainability and profit.
How Green Hydrogen Is Made
Hydrogen has promise as a fuel that burns without creating greenhouse gases. But the production of hydrogen isn’t necessarily as clean. Only 1% of current hydrogen production is produced from renewable sources, according to the International Energy Agency. The Wall Street Journal looks at some of the major production processes, which are often differentiated by color.
How Eastman Strives for a Circular Plastics Economy
“Mechanical recycling—where you go out and take items like single-use bottles, chop, wash and re-meld them and put them back into textiles or bottles—can only really address a small portion of the plastics that are out there,” Crawford said. After a few cycles, the polymers in the products degrade and the process is no longer possible.
Instead, Eastman uses advanced, also known as molecular or chemical, recycling. “We unzip the plastic back to its basic building blocks, then purify those building blocks to create new materials,” Crawford said. This “creates an infinite loop because that polymer can go through that process time and time again.”
Never Heard of Recycled Paint? You Have Now! - Dulux Trade Evolve
Seeq Accelerates Chemical Industry Success with AWS
Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, today announced agreements with two of the world’s premier chemical companies: Covestro and allnex. These companies have selected Seeq on Amazon Web Services (AWS) as their corporate solution, empowering their employees to improve production and business outcomes.
Colgate-Palmolive Focuses on Machine Health to Improve Supply Chain Operations
Colgate-Palmolive is feeding this wireless sensor data into Augury’s machine health software platform. Pruitt pointed out that this enables Colgate-Palmolive’s machine data to be compared with machine data from more than 80,000 other machines connected to the Augury platform around the world.
“That massive analytical scale brings us insights on how to optimize the performance of equipment and make ever-smarter choices on how and where we deploy it,” Pruitt said. “What’s possible only gets more compelling as this AI solution harnesses more data to create better health outcomes for our machines and our business.”
Providing a specific example of how Augury’s Machine Health system has helped Colgate-Palmolive, Pruitt noted that the system’s AI detected rising temperatures in the drive of a tube maker and alerted the plant team. “Upon inspection, they discovered a problem with the motor’s water cooling system,” he said. “By getting it quickly resolved, we prevented the drive from failing due to overheating, which would’ve stopped the tube production line and incurred replacement costs. We figure the savings at 192 hours of downtime and an output of 2.8 million tubes of toothpaste, plus $12,000 for a new motor and $27,000 in variable conversion costs.”
Survey: Data Analytics in the Chemical Industry
Seeq recently conducted a poll of chemical industry professionals—process engineers, mechanical and reliability engineers, production managers, chemists, research professionals, and others—to get their take on the state of data analytics and digitalization. Some of the responses confirmed behaviors we’ve witnessed first-hand in recent years: the challenges of organizational silos and workflow inefficiencies, and a common set of high-value use cases across organizations. Other responses surprised us, read on to see why.
Leveraging AI and Statistical Methods to Improve Flame Spray Pyrolysis
Flame spray pyrolysis has long been used to make small particles that can be used as paint pigments. Now, researchers at Argonne National Laboratory are refining the process to make smaller, nano-sized particles of various materials that can make nano-powders for low-cobalt battery cathodes, solid state electrolytes and platinum/titanium dioxide catalysts for turning biomass into fuel.