Using the right tools, chemical manufacturers can make sustainable step changes, such as for emissions reductions, while also increasing operational efficiency and product yield
The chemical process industries (CPI) are characterized by faithful devotion to tried-and-true procedures, yielding efficient production, reliability and repeatability. However, this same set of values can cause the industry to lag other process sectors in adopting new technologies to transform operations.
Despite this trend, chemical companies remain under intense pressure to continuously innovate, increase sales, reduce costs and grow profit margins to remain competitive. Simultaneously, they are navigating raw materials constraints, supply chain challenges, cost pressures and the need to maintain a skilled workforce. However, amid these trials, chemical manufacturers are being increasingly emboldened to set environmental goals towards decarbonization and sustainability, while maintaining profitability.
Sustainable manufacturing
Chemical manufacturers have been balancing these needs in North America dating back to the Clean Air Act of the 1970s. While regulations have evolved over time, the underlying ethos remains the same: maximize production and profitability while taking responsible care of the environment.
As organizations often pledge corporate sustainability targets to reduce their carbon footprint, manufacturers must maintain productivity levels to continue producing the everyday items that consumers demand. However, sustainability goals are sometimes ill-defined, leaving manufacturers wondering whether they are meeting required targets.
Emissions control is a prominent means of achieving sustainability targets, seeking to reduce the amount of pollutants emitted by combustion engines powered by fossil fuel sources. It also includes industrial emissions that occur from process industries.
The chemical manufacturing sector is responsible for 925 million metric tons of carbon dioxide (CO2) emissions, equal to about 2% of global generation, making emissions monitoring and reduction a critical area of focus for achieving net-zero targets [1]. Approximately 67% of greenhouse gas emissions industry-wide come from fuel combustion, while the other 33% are a result of actual industrial processes and product use. While emissions have decreased over time, these reductions have primarily occurred in the industrial processes.
Analytics for emissions monitoring
As global climate and political outlooks become increasingly complex, there is a need for further innovation in the realm of emissions control. Addressing this and other needs, data analytics provide incremental improvements to emissions monitoring and reduction efforts by providing measurable and meaningful insights that enable plant personnel to optimize operational efficiency.
By collecting and analyzing the right information from sensors and other time-stamped tag data sources, data analytics tools perform near real-time monitoring and actively identify anomalies or other issues indicative of impending exceedances of regulatory limits. These embedded predictive tools within advanced analytics platforms enable more efficient forecasting of emissions patterns based on historical data and other correlated factors (Figure 1).
FIGURE 1. By collecting and analyzing the right information from sensors and other data sources, data analytics tools perform near real-time monitoring and enable more efficient forecasting of emissions patterns based on historical data and other correlated factors
Throughout the chemicals sector, leading companies are using data analytics to optimize production, enhance quality and improve reliability through predictive maintenance, while also addressing sustainability through specific use cases.
How can we take the nebulous and broad topic of sustainability with its qualitative nature and make it quantifiable and actionable? After all, every goal must be measurable.
One way is by leveraging available software tools, resources and platforms to investigate how data analytics can quantify and prioritize environmental challenges that plague chemical manufacturing plants every day. Furthermore, new digital tools, such as artificial intelligence (AI) and machine learning, are making optimization more universally accessible, particularly to non-programmers.
CPI companies must manage ever-changing regulatory parameters, especially CO2 and other gas emissions. At present, 2030 CO2 reduction efforts are critical to have a shot at meeting global 2050 net-zero targets.
Case studies
Production yield and sustainability are not necessarily at odds, especially in the chemical sector. Oftentimes, enhancements in productivity, quality or reliability also yield greenhouse gas emission reductions and progress sustainability goals. The following case studies demonstrate the intertwining of operational optimization, decarbonization and emissions control.
Case 1. Compressor blowdown valve monitoring. A chemical manufacturer was obligated to track material release from a gas compressor blowdown valve. The calculation was performed periodically for reporting purposes. However, the calculation required weeks of an engineer’s time, as he or she manually aggregated, cleansed and contextualized all the data from numerous sources, identified the blowdowns and computed results. Because this procedure was so cumbersome, it was conducted minimally, causing the team to sometimes miss identifying process conditions that were causing the blowdowns.
By leveraging an advanced analytics and AI platform, the company was able to automatically and proactively monitor the blowdown valves to identify trip events and calculate emissions for environmental regulatory reporting. Using the platform, process conditions leading to blowdown events were recognized almost in real-time (Figure 2).
FIGURE 2. Using an advanced analytics and AI platform, a chemical manufacturer proactively identified impending blowdown events to signal the need for process changes, thereby reducing emissions
The company implemented blowdown occurrence and volume calculations for each of its systems, monitoring pressure transmitter values, along with derivatives of pressures, volumes and equipment data. This strategy was replicated across all compressors throughout the facility. The information provided rapid identification and mitigation of unfavorable conditions, reducing the frequency of compressor blowdowns that otherwise may not have become visible for nearly a year. The actions resulted in an estimated 20% reduction in methane emissions company-wide.
Case 2. Distillation soft sensor column. A leading specialty chemical manufacturer operates facilities globally, and in one of its plants, it operates a distillation column to separate out gas product streams. Historically, the company’s quality-control team estimated the concentration of the distillation column’s top product three times each day. The team’s objective was to minimize specific consumption of raw materials by estimating the top products.
This soft sensor implementation predicted product concentration at the top of the column by analyzing the top temperature and pressure alongside setpoints. The company implemented advanced analytics to monitor the accuracy and avoid performance losses at the top of the column. This led to decreased raw material usage in addition to a 3% reduction in CO2 emissions.
Case 3. Nitrogen blanket balancing. Another specialty chemical company uses nitrogen blanketing in tanks to remove air and prevent combustion. Nitrogen blanketing is frequently exercised to maintain safety with volatile solvents. However, if not operated properly, it can result in additional fugitive emissions. These emissions are typically caused by oscillations due to poor control settings, leaks, open valves or faulty control valves.
Additionally, blanket systems on interconnected vessels sometimes compete. For this situation, the company used advanced analytics to identify steady-state operation and to locate the valves that were operating abnormally.
As material is transferred to and from each vessel, the valves open and close. The company monitored these events in each unit to determine average operating windows and the standard deviation of valve positions each day when a train was not in use. The company classified any conditions outside the normal-defined operating window as abnormal operations. Using an analytics platform’s dashboard, plant personnel were notified when faulty blanket control valves were unnecessarily venting nitrogen (Figure 3).
FIGURE 3. A specialty chemical company used analytics data to identify faulty blanket control valves that were unnecessarily venting nitrogen
The engineering teams were able to quickly review the dashboard each week, revealing that each faulty valve was responsible for an average greater than $100,000 in wasted nitrogen and evaporated volatile solvent.
Case 4. Standardizing support among an entire customer base. Honeywell UOP has a long history within the chemicals industry as a leader in licensing technologies for refining, petrochemical and gas customers globally. The company provides technical services and manufactures catalysts and adsorbents for its licensed technologies.
As a technology licenser, Honeywell UOP has over 3,000 installations of its process equipment, consisting of more than 6,000 units utilizing catalysts and adsorbents. With such a large installed base, the company must process massive amounts of data.
The licensor helps its customers operate their units more efficiently and reliably, but tracking progress across every unit is a herculean task, requiring a monitoring solution to support its customers quickly, accurately, and while minimizing the strain on limited internal employees.
The company achieved this result using an advanced analytics platform to push data, analytics and templates across its suite of connected solutions among its entire customer base, as part of a consistent deployment with minimal incremental effort. The team designs its solutions with different layers of screen views depending on customer persona so each can visualize detail that is meaningful to them.
By leveraging this single point of access in the advanced analytics platform, the company shifted its data from Excel-based files to templatized troubleshooting tools, data reviews and analysis with automated reporting. They estimated efficiency gains of $1.5 million due to this platform migration.
Improving through digitalization
The case studies in this article exemplify applications within chemical manufacturing. Yet, they showcase how advanced analytics can be leveraged across all process industries to improve productivity and sustainability through more efficient operations, yielding quantifiable and impactful results.
Additionally, they shine light on the value of human time, with automated technologies freeing up process experts to focus more deeply on analyzing data and recognizing patterns, along with discussing with plant managers how to best improve operational efficiency. The results indicate that the right technologies help support overall corporate sustainability strategy and goals. Advanced analytics platforms help manufacturers better monitor, predict and reduce emissions, while also fostering compliance and improved operational efficiency. ■
Edited by Dorothy Lozowski
Reference
1. McKinsey & Company, Decarbonizing the chemical industry, www.mckinsey.com/industries/chemicals/our-insights/decarbonizing-the-chemical-industry, April 2023.
Author
Janelle Armstead-English is the industry principal for chemicals at Seeq Corp. (113 Cherry St., PMB 78762, Seattle, WA 98104; Email: [email protected]; Phone: 206–801–9339). She has an engineering, market research, sales, and product management background with a dual B.S. degree in chemical engineering and mathematics from the University of Pittsburgh. Armstead-English has nearly two decades of experience working with various chemical manufacturers like Honeywell UOP and Praxair (now Linde). In her current role, she enjoys analyzing the ever-changing chemicals market and understanding the challenges around digital transformation for chemicals customers.