Chemical
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.
Assembly Line
New reactor could save millions when making ingredients for plastics and rubber from natural gas
The researchers’ new reactor system efficiently makes propylene from shale gas by separating propane into propylene and hydrogen gas. It also gives hydrogen a way out, changing the balance between the concentration of propane and reaction products in a way that allows more propylene to be made. Once separated, the hydrogen can also be safely burned away from the propane, heating the reactor enough to speed up the reactions without making any undesirable compounds.
Because the hydrogen can be burned inside the reactor and can operate under higher propane pressures, the technology could allow plants to produce propylene from natural gas without installing extra heaters. A plant that produces 500,000 metric tons of propylene annually could save as much as $23.5 million over other methods starting with shale gas, according to the researchers’ estimates. Those savings come on top of the operational savings from burning hydrogen produced in reaction, rather than other fuels.
Global self-optimizing control of batch processes
This work considers to achieve near-optimal operation for a class of batch processes by employing self-optimizing control (SOC). Comparing with a continuous one, a batch process exhibits stronger nonlinearity with dynamics because of the non-steady operation condition. This necessitates a global version of SOC to achieve satisfactory performance. Meanwhile, it also makes the existing global SOC (gSOC) not directly applicable to batch processes due to the causality amongst variables. Therefore, it is necessary to extend the original gSOC to batch processes. In addition to the nonconvexity challenge of the original gSOC problem, the new extension for batch processes has to face even more challenges. Particularly, the causality due to dynamics of batch processes brings in structural constraints on controlled variables (CVs), making a CV selection problem even more difficult. To address these challenges, the gSOC problem is recast in a vectorized formulation and it is proved that the structural constraints considered are linear in the vectorized formulation. Moreover, a novel shortcut method is proposed to efficiently find sub-optimal but more transparent solutions for this problem.
There are two main types of online optimization technologies for batch processes, namely run-to-run/batch-to-batch optimization and within-batch optimization. The former is based on the repetitiveness of the batch process to iteratively update batch-to-batch operations. Optimization actions for the next batch are carried out upon the completion of the previous batch, aiming to achieve incremental performance improvements throughout the iterations. Iterative Learning Control (ILC) is a widely recognized method in this field. However, a shortcoming of this type of approaches is its failure to account for uncertainties that arise within a single batch process. As a result of such uncertainties, the behavior of the same process can vary from one batch to another, rendering knowledge learned from one batch inapplicable to another. This limitation makes ILC approaches unsuitable for such processes.
Mitsui-Celanese JV commences production of methanol derived from CO2
Fairway Methanol LLC, a US-based 50-50 joint venture between Mitsui & Co., Ltd. (Tokyo) and Celanese Corp. (Dallas, Tex.), has begun the production of methanol by using carbon dioxide (CO2) emitted from plants surrounding the joint venture’s facility. Fairway Methanol is expected to capture 180 thousand metric tons of CO2 and produce 130 thousand metric tons of low-carbon methanol per year, which leads its annual production capacity to 1.63 million metric tons per year.
Cognite Announces Beta Launch of Generative AI-Powered Remote Operations Control Room for Celanese Clear Lake Facility
Cognite, a globally recognized leader in industrial software, today announced the beta launch of a generative AI-powered Remote Operations Control Room (ROCR) at the Celanese facility in Clear Lake, Texas. Celanese, a global chemical and specialty materials company, plans to use the ROCR to deliver full visibility into the real-time operation of its sites worldwide, thereby expediting workflows and gaining operational insights orders of magnitude more efficiently.
By integrating generative AI into a Remote Operations Control Room, Cognite will increase visibility to our site leaders and their teams and enable a multitude of possibilities – from monitoring equipment performance to enhancing root cause analysis to streamlining and enhancing our processes,” said Brenda Stout, vice president of Acetyls Manufacturing at Celanese.
Value Based Product Development
People in “tech” like to talk “product market fit” and I think it does well in software as a service (SaaS) business, but it’s a dangerous philosophy to adopt in chemicals. Product market fit makes it sound like a product will work for everyone if you can just figure it out (e.g., Salesforce, Uber, Facebook), get scale to take advantage of network effects, and then iterate on new products with near zero production costs. To some degree this might be true for commodity chemicals, but the margins on commodities are often so narrow that your investors will be pissed off that you are trying to eke out a 30% gross margin business (how are you going to 10x that $5 million dollar check if you are getting such small profits? Scale? Tough sell I think).
The question is, do you have enough time to do value-based product development where it might take 5+ years from product launch (all technical and engineering work is finished) to make it a profitable business?
Celanese's Vision for an Autonomous, Self-Optimizing Plant Powered by Generative AI
One of the key things we’ve been planning to do in 2023 is scaling the (Cognite) platform, bringing all the data together, putting the right context, the right meaning to it, getting it contextualized and modeling it. As part of that investment, we’re using artificial intelligence and generative AI capabilities. But our artificial intelligence journey or generative artificial intelligence is only as good as our underlying data. So, the biggest effort for us has been to standardize the data on common data models, bring it all together, contextualize it and then start leveraging AI capabilities on top of that.
You have to make sure that whatever you’re architecting actually is intuitive and works and addresses the needs of the people. For example, you have this phone, right? I don’t need a user manual or training for this. It just works, and I am married to it. I can’t live without it. So we have to find the balance of making the right solutions for the people and keeping that in mind. Also, we have developed what we call a Digital Manufacturing Academy that is now available globally for all our users. And that academy is really around giving people the ability to upskill, have more data literacy, more digital literacy skills, and even give people the opportunity to start learning how to code, if they need to.
Development of ultra-fast computing method for powder mixing process
Powder mixing is an important operation in many industries. Numerical simulations using the discrete element method (DEM) have been widely used to analyze powder-mixing processes. However, one of the current limitations of the DEM simulation is its high computational cost. Recently, approaches that combine machine learning models and numerical simulations have attracted considerable attention for high-speed computing. However, there has been no research on high-speed computing methods for powder mixing that account for individual particle motions. Here, we propose an original machine learning model, namely, a recurrent neural network with stochastically calculated random motion (RNNSR), which enables a long-time-scale powder mixing simulation with low computational cost and high accuracy. The RNNSR is designed to learn individual particle dynamics with periodicity from short-term DEM simulation results and predict powder mixing for a longer period. The RNNSR combines a recurrent neural network and a stochastic model to predict both convective and diffusive mixing. The simulation results obtained using the RNNSR were quite similar to those obtained using the DEM in terms of the degree of powder mixing, particle velocity, and granular temperature. It was also demonstrated that the RNNSR has the capability of ultrafast computing in powder-mixing simulations. In conclusion, we demonstrated the effectiveness of the RNNSR for ultrafast computation of the powder mixing process.
🛢️🧠ENEOS and PFN Begin Continuous Operation of AI-Based Autonomous Petrochemical Plant System
ENEOS Corporation (ENEOS) and Preferred Networks, Inc. (PFN) announced today that their artificial intelligence (AI) system, which they have been continuously operating since January 2023 for a butadiene extraction unit in ENEOS Kawasaki Refinery’s petrochemical plant, has achieved higher economy and efficiency than manual operations.
Jointly developed by ENEOS and PFN, the AI system is designed to automate large-scale, complex operations of oil refineries and petrochemical plants that currently require operators with years of experience. The new AI system is one of the world’s largest for petrochemical plant operation according to PFN’s research, with a total of 363 sensors for prediction and 13 controlled elements. The companies co-developed the system to improve safety and stability of plant operations by reducing dependence on technicians’ varying skill levels.
Fixing the Haber–Bosch process
But the Haber–Bosch process hasn’t changed all that much since its discovery more than 100 years ago. The process uses iron or ruthenium catalysts to react hydrogen and nitrogen together under extreme conditions. Temperatures can reach 600°C, with pressures raised to over 200 times that of the Earth’s atmosphere.
Transforming ammonia production is likely to move in two stages. The first involves adapting current production methods so that green hydrogen can be used as a feedstock, with renewable electricity used to power the plants. Further into the future, new methods that rely on completely different chemistry could come online.
The very high pressures associated with Haber–Bosch help to maximise the amount of nitrogen and hydrgeon that is converted into ammonia in a single pass, without having to be fed back into the reactor. In current facilities, the compression systems are based on steam that is a byproduct of the reaction that makes the hydrogen feedstock from fossil hydrocarbons. But if the process is to be based on green hydrogen, it would make sense to use much more energy-efficient electric compressors.
⚗️ Industry consortium to develop modern chemical manufacturing methods
A major consortium led by Imperial and chemical company BASF is to help make chemical manufacturing more efficient, resilient, and sustainable. Imperial will receive ÂŁ17.8 million from the Engineering & Physical Sciences Research Council (EPSRC) and industry partners under the EPSRC Prosperity Partnership programme in a consortium of organisations from across the chemicals value chain.
“Flow chemistry is inherently more sustainable than batch processing because it makes better use of heat and materials,” said lead investigator Professor Mimi Hii from Imperial’s Department of Chemistry. “It can also provide a powerful tool for automating production and the research and development of more sustainable processes. However, there are technical bottlenecks that are holding back its full implementation. Through this new consortium we will be in a strong position to address these.”
In a World First, Yokogawa’s Autonomous Control AI Is Officially Adopted for Use at an ENEOS Materials Chemical Plant
ENEOS Materials Corporation (formerly the elastomers business unit of JSR Corporation) and Yokogawa Electric Corporation (TOKYO: 6841) announce they have reached an agreement that Factorial Kernel Dynamic Policy Programming (FKDPP), a reinforcement learning-based AI algorithm, will be officially adopted for use at an ENEOS Materials chemical plant. This agreement follows a successful field test in which this autonomous control AI demonstrated a high level of performance while controlling a distillation column at this plant for almost an entire year. This is the first example in the world of reinforcement learning AI being formally adopted for direct control of a plant.
Over a 35 day (840 hour) consecutive period, from January 17 to February 21, 2022, this field test initially confirmed that the AI solution could control distillation operations that were beyond the capabilities of existing control methods (PID control/APC) and had necessitated manual control of valves based on the judgements of experienced plant personnel. Following a scheduled plant shut-down for maintenance and repairs, the field test resumed and has continued to the present date. It has been conclusively shown that this solution is capable of controlling the complex conditions that are needed to maintain product quality and ensure that liquids in the distillation column remain at an appropriate level, while making maximum possible use of waste heat as a heat source. In so doing it has stabilized quality, achieved high yield, and saved energy.
The Role of Industrial AI in Chemical Manufacturing Digitization
In order for chemical manufacturers to optimize production lines, they need to address different process inefficiencies, such as the formation of undesired side products, process instabilities, losses due to impurities and more, on an ongoing basis. Given the complexities of chemical manufacturing, it’s extremely time-consuming and difficult to understand the root causes for these process inefficiencies, let alone anticipate when they are going to happen. Often times, it is the specific behavior of the combination of multiple production parameters, or tags, that cause the inefficiency to happen.
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.
Eastman to invest up to $1 billion to accelerate circular economy through building world’s largest molecular plastics recycling facility in France
Eastman plans to invest up to $1 billion in a material-to-material molecular recycling facility in France. This facility would use Eastman’s polyester renewal technology to recycle up to 160,000 metric tonnes annually of hard-to-recycle plastic waste that is currently being incinerated.
The investment would recycle enough plastic waste annually to fill Stade de France national football stadium 2.5 times, while also creating virgin-quality material with a significantly lower carbon footprint. Eastman is the largest investor at this year’s “Choose France” event, which is focused on attracting foreign investment to France.
This multi-phase project includes units that would prepare mixed plastic waste for processing, a methanolysis unit to depolymerize the waste, and polymer lines to create a variety of first-quality materials for specialty, packaging, and textile applications. Eastman also plans to establish an innovation center for molecular recycling that would enable France to sustain a leadership role in the circular economy. This innovation center would advance alternative recycling methods and applications to curb plastic waste incineration and leave fossil feedstock in the ground. The plant and innovation center would be expected to be operational by 2025, creating employment for approximately 350 people and leading to an additional 1,500 indirect jobs in recycling, energy and infrastructure.
Evonik builds world's first industrial-scale production plant for rhamnolipids
Evonik is investing a three-digit million-euro sum in the construction of a new production plant for bio-based and fully biodegradable rhamnolipids. The decision to build the plant follows a breakthrough in Evonik’s research and development. Rhamnolipids are biosurfactants and serve as active ingredients in shower gels and detergents. Demand for environ-mentally friendly surfactants is growing rapidly worldwide.
The investment at the Slovenská Ľupča site in Slovakia strengthens Evonik’s partnership with the consumer goods group Unilever, which began in 2019. At the same time the investment allows Evonik to further expand its own market position in the growth market for biosurfactants. The new plant is scheduled to come on stream in two years.
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.”
Five companies make a quarter of world’s single use plastics
The top 5 companies created roughly 26 million metric tones of plastic waste fueled by demand of the United States and China.
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.
On Factory Floors, a Chime and Flashing Light to Maintain Distance
Businesses like Henkel, a big German chemical company, are trying wearable sensors to prevent virus outbreaks among workers.
A Case of Applying AI to an Ethylene Plant
Unexpected equipment failures or maintenance may result in unscheduled plant shutdowns in continuously operating petrochemical plants such as ethylene plants. To avoid this, the operation status needs to be continuously monitored. However, since troubles in plants have various causes, it is difficult for human workers to precisely grasp the plant status and notice the signs of unexpected failures and need for maintenance. To solve this problem, we worked with a customer in an ethylene plant and developed a solution based on AI analysis. Using AI analysis based on customer feedback, we identified several factors from numerous sensor parameters and created an AI model that can grasp the plant status and detect any signs of abnormalities. This paper introduces a case study of AI analysis carried out in an ethylene plant and the new value that AI technology can offer to customers, and then describes how to extend the solution business with AI analysis.
Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process
This paper explores a reinforcement learning (RL) approach that designs automatic control strategies in a large-scale chemical process control scenario as the first step for leveraging an RL method to intelligently control real-world chemical plants. The huge number of units for chemical reactions as well as feeding and recycling the materials of a typical chemical process induces a vast amount of samples and subsequent prohibitive computation complexity in RL for deriving a suitable control policy due to high-dimensional state and action spaces. To tackle this problem, a novel RL algorithm: Factorial Fast-food Dynamic Policy Programming (FFDPP) is proposed. By introducing a factorial framework that efficiently factorizes the action space, Fast-food kernel approximation that alleviates the curse of dimensionality caused by the high dimensionality of state space, into Dynamic Policy Programming (DPP) that achieves stable learning even with insufficient samples. FFDPP is evaluated in a commercial chemical plant simulator for a Vinyl Acetate Monomer (VAM) process. Experimental results demonstrate that without any knowledge of the model, the proposed method successfully learned a stable policy with reasonable computation resources to produce a larger amount of VAM product with comparative performance to a state-of-the-art model-based control.
Designing Basic & Detailed Processes for Mega Methanol Plants
A thorough understanding of the processes required for a methanol plant is necessary for the selection of pumps, valves and hoses that will give life to the production facility. Specifying the types and grades of equipment required at the plant was a collaborative effort between Gabriel and the product specific experts.
The number of valves used in a mega methanol plant exceeds expectations. When asked, Gabriel expressed that “there are literally thousands of valves involved in a project like this one. There are probably 1,200 automated control valves in this plant, and that estimate does not take into account any of the manual valves used throughout the facility.” The valves he found to be the most engaging were critical process valves; high pressure steam valves, gas valves, methanol valves and oxygen valves. Each of these valve types requires extra attention and are typically custom made to suit the projects needs.