M. Pietryka, Seeq, Denver, Colorado
Industrial sustainability is a hot topic as the world grapples with climate change and evolving ecological trends. According to an Accenture study, only 18% of companies are on track to reach global net-zero aspirations by 2050; however, despite this sluggish progress, 77% of companies studied have reduced their operational emissions intensity in the past decade.1
This information demonstrates that processors are cutting carbon emissions, just not quickly enough. Furthermore, the strategies being deployed throughout industry to reduce emissions and decarbonize operations are working, but they must be accelerated to meet carbon mitigation targets.
To accelerate progress toward corporate sustainability goals and commitments—without expensive and drawn-out capital projects—processors are deploying digital technologies at scale, specifically advanced analytics platforms. These tools enable companies to:
Streamline access to multiple data sources
Improve energy efficiency and minimize waste
Create holistic process insights
Automate regulatory reporting
Reduce greenhouse gas (GHG) emissions.
Still, many organizations are struggling to get started on their sustainability initiatives, which prevents them from establishing a detailed roadmap and articulating pathways to success.
As companies look to begin their journey, a multifaceted approach that combines various decarbonization levers is typically the most successful. These levers include switching to renewable energy sources, optimizing process efficiency, exploring new business models, and promoting sustainable employee and customer behavior changes, among others.
From the foundations of measuring emissions in real time, to forecasting the impact of each lever and automating reporting, digital technologies help processors track key sustainability metrics and overall progress in the journey to net-zero.
Streamlining access to multiple data sources. To adhere to rapidly changing industry standards and maintain compliance with complex regulatory reporting requirements at equipment, site and global levels, companies must often implement and rely on data from multiple different systems. Without the right unifying software, aggregating data from these disparate process historian, lab information management, enterprise resource planning, manufacturing execution and other systems is nearly impossible, making it difficult and time-consuming to accurately report on process efficiency and emissions.
Advanced analytics platforms address this and other issues by centralizing data from these various systems, and providing context and a unified environment for aggregating, monitoring and analyzing it collectively. Some of the largest deployments around the world feature more than 200 previously disparate systems with live connectivity via these types of software platforms, all without the need to move or copy data from each individual system of record. This empowers users to query information in place and on-demand, providing seamless operational technology (OT) integration while maintaining data integrity.
In addition to data connectivity, enterprise emissions monitoring and reporting ecosystems typically require the integration of various reporting, supplier management, lifecycle assessment and other software tools. These integrations are critical because they provide users at varying levels throughout a company—from the C-suite to the plant floor—with access to the right data for making informed operational decisions.
For example, Chevron’s Salt Lake City refinery (U.S.) built a custom export tool within an advanced analytics platforma—their advanced analytics platform—to extract final emissions data and format it for ingestion into its corporate GHG reporting software.2 The team also used the analytics platform to connect to multiple other software systems at the site, and feed data to the same reporting software.
With these types of connections and integrations, industrial companies can access operational data from their sites’ enterprise-wide systems. Advanced analytics platforms take care of cleansing and contextualization, empowering subject matter experts (SMEs) to monitor process efficiency, assess patterns, conduct root cause analyses (RCAs) and automate reporting up to corporate and regulatory levels.
Automating regulatory reporting. In the past—due to the difficulty of manually accessing, cleansing and aggregating data needed for regulatory reporting—many companies only gathered data for emissions reporting on a monthly or quarterly basis. This retrospective method made process optimization and minimizing emissions difficult because often, only aggregate numbers were provided, rendering it impossible to drill down and pinpoint issue root causes.
Midstream oil and gas operator Kinder Morgan recently used the author’s company’s advanced analytics platforma to extract required data from its continuous emissions monitoring systems and other analyzers, enabling its SMEs to generate calculations and compare computed values against semiannual and annual reporting figures (FIG. 1). The legacy process required one or more months of lag time between potential deviation occurrences and confirmation. However, since implementing the advanced analytics platforma, reporting and confirmation occurs daily, enabling plant personnel to substantially speed up root cause investigations.
This also provides clearer troubleshooting details when issues arise, facilitating quicker corrective actions and mitigations. In addition to reducing the time required to calculate emissions and GHG reconciliation information, these improvements increased process efficiencies and decreased total emissions.
Improving energy efficiency. To capture the full value of advanced analytics, teams must act on insights generated to optimize their operations. Common examples include improving energy consumption efficiency and minimizing flaring, blowdowns and steam venting, all of which indirectly or directly reduce GHG emissions to the atmosphere.
Aker BP, a Norwegian oil and gas company, used the monitoring and analytics tools within the proprietary platforma to optimize gas turbine efficiency.3 The team began by applying native automatic data preprocessing and cleansing algorithms in the platform to build predictive models for expected delta pressure (dP) within the system, which negatively increases turbine emissions. The predicted dP was then compared to the actual dP, alongside a forecast cost of degradation—in terms of emissions and maintenance cost—to determine ideal times to replace air filters (FIG. 2). This prediction was then scaled across plantwide assets.
The initial deployment reduced CO2 emissions by 10,000 tpy across six assets. Additionally, implementing the analytics platform empowered the organization to transition from calendar-based to condition-based maintenance for its gas turbines.
Minimizing waste. Another way to decrease emissions is to improve circularity and reduce waste. Waste can occur in the form of off-specification products, process chemical byproducts, volatile organic compounds (VOCs) and process wastewater, among others.
Syngenta, an agrochemical manufacturer, used the same advanced analytics platforma to monitor its nitrogen blanket balancing.4 Because the company works with flammable solvents, its operations require nitrogen balancing to increase safety and ensure the absence of oxygen in its vessels. Nitrogen balancing is done using two valves: one to add nitrogen when additional pressure is required, and the other to vent nitrogen when a vessel is over-pressurized.
Phase tools within the advanced analytics platforma are used to determine when the vessel is in steady-state mode, and to identify whenever any valve is operating abnormally. Correct balancing is critical because every vessel is interconnected, which means a faulty control valve can create a ripple of problems. Improper balancing can cause emissions issues, solvent losses and nitrogen deficiencies.
Deploying the analytics platform has empowered Syngenta’s engineering teams to monitor valve performance weekly and fix faulty valves earlier, saving the company ~€120,000 (~$132,000) in lost nitrogen costs per faulty valve every year. Additionally, the company is curbing fugitive emissions from solvents, consequently reducing its carbon footprint and increasing raw material usage efficiency.
Shaping the future. According to the World Economic Forum, digital technologies can reduce emissions by 20% when implemented at scale.5 Achieving this requires both buy-in from the workforce, as well as the skillsets to adopt digital technologies into daily workflows. This combination enables teams to solve the challenges they are confronted with in innovative ways.
By providing SMEs with access to industrial analytics platforms that accelerate pathways to insights, processors can do more with their data and make decisions that bolster process efficiency and promote sustainability. This powerful connection between SMEs and the insights derived from data empowers vision-casting, which helps guide organizations to achieving sustainability targets.
With 2050 rapidly approaching, digital transformation cannot wait. To meet ambitious net-zero goals, industrial organizations must leverage analytics advancements that reduce manual data cleansing needs, promote deep insight generation, automate regulatory reporting, and facilitate efficiency improvements and waste reduction. Ongoing industrial sustainability depends on this shift. HP
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Seeq
LITERATURE CITED
Accenture, Destination net-zero, 2023, online: https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/NetZero-Report-FNL-112823.pdf#zoom=40
Chevron, “Seeq enables Chevron to streamline greenhouse gas emissions reporting,” Chevron Technology Ventures, online: https://f.hubspotusercontent10.net/hubfs/3808865/CHEVRON%20TECHNOLOGY%20VENTURES%20PDF.pdf
Raoul, S., “Reducing emissions through gas turbine optimization,” Conneqt 2024, May 2024, online: https://engage.seeq.com/conneqt-2024-recap/oil--gas/reducing-emissions-through-gas-turbine-optimization?fw=9a89a
Abel, J., “Syngenta uses Seeq advanced analytics to drive sustainability and performance goals,” ARC Advisory Group, online: https://engage.seeq.com/conneqt-2024-recap/oil--gas/reducing-emissions-through-gas-turbine-optimization?fw=9a89a
George, M., K. O’Regan and A. Holst, “Digital solutions can reduce global emissions by up to 20%. Here’s how,” World Economic Forum, May 23, 2022, online: https://www.weforum.org/agenda/2022/05/how-digital-solutions-can-reduce-global-emissions/
Mark Pietryka is a Senior Analytics Engineer at Seeq. He has an engineering background and earned a BS degree in chemical and biomolecular engineering from North Carolina State University (U.S.). Pietryka has nearly a decade of experience working for and with major oil and gas companies to solve high-value business problems. In his current role, he enjoys supporting industrial organizations as they maximize value from their time series data.