Improving sustainability with advanced analytics applications
Three case studies demonstrate the ways process manufacturers are leveraging their advanced analytics applications to promote sustainable operations and business practices.
- Sustainability has been recognized as an area of significant importance in the process industries, but many organizations face challenges accessing and connecting data from siloed systems.
- Advanced analytics software provides process manufacturing organizations with the power of a single platform to enable company-wide learning, best practice dissemination, and collaboration.
In the expanding realm of digital technologies for process control systems, supervisory control and data acquisition (SCADA), distributed control, laboratory information management (LIMS) and other systems have been monitoring, gathering, and processing data in real time for decades. Using these seemingly-endless streams of process data, facility subject matter experts (SMEs) can identify situations requiring responsive action, in addition to opportunities for operational optimization, helping teams progress toward organizational initiatives, including those related to sustainability.
Sustainability has been recognized as an area of significant importance in the process industries, but many organizations face challenges accessing and connecting data from the aforementioned systems, which are often siloed, by analyzing the data and operationalizing insights. Addressing these challenges, today’s advanced analytics solutions empower process manufacturers to find, share and act on insights derived from time-series data, helping operations and engineering teams shift toward proactive approaches that drive sustainable practices.
Consider these case studies, which demonstrate how advanced analytics solutions enabled organizations in different process industries to achieve key sustainability milestones across top initiatives in three major categories—efficiency and impact, reporting and net zero pledge.
Data visibility provides opportunities to minimize environmental impact
Identifying opportunities for environmental improvement is the first step toward change, and the next is creating sustainability-related key performance indicators. Benchmarking is invaluable when identifying performance targets, which are often derived from plant models, simulators and optimizers. This is best-accomplished with near-real-time plant data in a common enterprise-wide platform.
Advanced analytics software provides process manufacturing organizations with the power of a single platform to enable company-wide learning, best practice dissemination and collaboration. Built for live connectivity to SCADA systems, LIMS, historians and other databases, these solutions provide simplified data-cleansing and contextualization tools, empowering SMEs to derive plantwide insights quickly.
Justifying an idle boiler
To reduce the amount of wasted energy and carbon emissions, process manufacturers require methods to identify time periods of wasteful operation, such as excessive electricity consumption or vented steam. This waste can be quantified as either a financial loss or CO2 emissions equivalent, providing common benchmarks for comparing alternative operating strategies.
A major refining company leveraged advanced analytics application to justify idling one of the boilers in a dual-boiler operation during the warm months of the year. The company’s SMEs configured the platform to identify time periods when the dual boiler system was operating at minimum firing rates while venting steam. The team aggregated potential annualized steam savings by examining these periods (Figure 1).
The SMEs then analyzed historical data to understand the probability of a boiler trip, which could have a significant financial impact in a single boiler operation, and weighed the potential steam cost and energy savings against the risk—defined as failure probability multiplied by financial consequence—of running a single boiler.
This analysis provided the necessary data to justify idling one of the boilers during prolonged periods of warm ambient weather, saving the refiner an average of $500,000 per year in vented steam costs. This operational change also reduced the company’s carbon footprint by decreasing energy required to run the boiler system.
Automated data conditioning and reporting saves SMEs valuable time
The day-to-day workload for most data analysts and process engineers is full of manual data prep and cleansing. It often requires using spreadsheets for analysis, which is time-consuming, cumbersome and filled with contextual barriers, preventing deep analysis of broad business processes necessary to increase efficiency and profitability.
By leveraging advanced analytics applications to automate data conditioning and subsequent reporting, companies can free up large periods of their SMEs’ valuable time, which can instead be spent optimizing operations and improving plant efficiency. This typically reduces both operating expenditures and emissions.
A major oil and gas company sought to automate regulatory compliance reporting of greenhouse gas emissions from refineries throughout its enterprise. To automate the workflow, the company’s SMEs leveraged advanced analytics to access data from refinery historians and apply calculations and contextualization for quarterly regulatory emissions reporting. Extensibility features within the application enabled the SMEs to build a custom solution for extracting final emissions data, formatting it for direct ingestion into its corporate greenhouse gas reporting software.
Leveraging automatic calculations and incorporating real-time data updates, the company cut the time required to conduct analysis time from two or three days down to just a few hours. This up-to-date and readily-available emissions performance information also enabled the company to take a proactive approach to emissions identification, sometimes resulting in prevention, rather than reporting after the fact.
Enabling teams to communicate emissions performance more efficiently
Without visibility into their environmental data, process manufacturers have a hard time understanding emissions quantity, as well as defining scope performance when limited to standard emissions data from their suppliers. Advanced analytics applications significantly ease connecting to and visualizing data from multiple sources, resulting in richer and more accurate performance metrics in a timely manner.
Reducing carbon emissions
A global chemical manufacturer recently pledged to reduce its carbon intensity in half by 2030. The company’s first step toward this ambitious goal was understanding the current state of its operations. Historically, this analysis was cumbersome and only conducted once per year. However, carbon intensity calculations are key to understanding the overall carbon footprint of a process.
The engineers used advanced analytics gained real-time awareness of utility stream carbon intensity. The software performed this calculation by converting process sensor data into carbon mass equivalents, providing SMEs the ability to easily compare current carbon intensity with targets for a given production quantity. Breaking the carbon footprint into individual utility streams empowered the operations team to identify the largest contributors, along with the methods to combat them.
These near-real-time carbon intensity estimates enabled the chemical manufacturer to make data-driven decisions to target carbon reduction on an ongoing basis and is making steady and measurable progress toward the goal.
Achieving sustainability initiatives
With threats to natural resources of many kinds, increasingly stringent regulatory requirements, and the ever-growing public priority on sustainable practices, production efficiency and emissions reduction strategies are more vital than ever for companies to thrive. This is almost impossible for the technologies and computational methodologies of yesteryear, but modern advanced analytics platforms help technical teams centralize data, generate insights, automate reporting, identify inefficiencies and visualize opportunities for performance improvements.
More process manufacturers are leveraging data and analytics in their quest for operational excellence, but relatively few are using it for sustainability initiatives. This cannot be overlooked any longer because environmental impact and recognition play just as big a role on the bottom line as high throughput in today’s global landscape. By applying advanced analytics to data enterprise-wide, companies can position themselves well to meet illustrious carbon neutrality and similar goals for the future.
Morgan Bowling, industry principal, Seeq. Edited by David Miller, Content Manager, Control Engineering, CFE Media and Technology, firstname.lastname@example.org.