Building energy resilience in heavy industry through forecast-driven optimization
03/12/2025 ABB Limited (Zurich)
Operational instability is no longer just inconvenient for heavy industry. It’s also costly and often unsafe. Refineries, power plants and chemical producers are operating in unpredictable energy markets while aiming to meet ambitious decarbonization goals.
In this environment, resilience has become essential.
Today, resilience is more than back up capacity. Increasingly it means considering forecast-driven optimization, intelligent closed-loop systems and fault response, with the capabilities that anticipate system changes and adapt operations proactively – all to keep energy systems stable when conditions fluctuate.
At the center of this shift are integrated energy management platforms that use analytics, automation and optimization to safeguard uptime and efficiency.
Why forecast-driven optimization matters
The International Energy Agency (IEA) notes that industry accounts for 37 percent of global final energy consumption[1]. Yet industrial energy systems remain vulnerable to sudden disruptions, from equipment malfunctions to grid instability. Even minor disturbances in steam or combined heat and power systems can escalate into costly outages.
Traditional maintenance approaches such as manual monitoring or after-the-fact adjustments are outdated. Optimization, powered by digital monitoring, AI-enabled forecasting and closed-loop model-based analytics, enables operators to anticipate degradation, predict demand and intervene proactively.
For energy-intensive operations, just a small sample of relevant analytics can improve asset availability and reliability, meaning fewer shutdowns, steadier supply and safer working conditions.
Steam and power networks highlight the challenge of balancing reactive and forecast-driven optimization. Facilities often run multiple boilers and turbines with different efficiencies, fuels and dynamics. Conventional control systems can struggle to balance these variables under changing demand and market conditions. However, advanced energy management processes can apply model predictive control and real-time optimization to coordinate assets dynamically. According to ABB[2], this approach delivers smoother operations and measurable savings of three to five percent on steam and CO? costs, often paying back in under a year.
Extending forecast-driven optimization across industries
Resilience also depends on how quickly systems respond when conditions change. In high-risk environments, operators are not always able to act fast enough. Intelligent control mitigates these risks by automatically adjusting setpoints and balancing load across assets.
AI-enabled, closed-loop systems can anticipate these disturbances, optimize steam use and maintain capture rates automatically, reducing the need for operator intervention and cutting regeneration energy costs while stabilizing performance.
Beyond individual technologies, forecasting is reshaping how wider mission-critical sectors think about resilience. In oil and gas, advanced monitoring of turbines and compressors allows operators to identify wear long before it results in failure. In power generation, model-based control of boilers and turbines helps plants remain stable under fluctuating fuel quality and growing shares of variable renewable input. In chemicals and heavy manufacturing, predictive analytics aligns process efficiency with emissions reduction goals, directly linking operational performance to an organization’s sustainability goals.
Another important dimension is workforce impact. As forecast-based systems automate routine adjustments, operators are freed to focus on higher-level strategy and oversight. This reduces the risk of human error under pressure and builds a culture where safety and efficiency are embedded into daily operations. The result is not only fewer process disruptions but also workplaces that are safer, more productive and better prepared for the net-zero transition.
Case studies: Wintershall Dea Oil Production and Cabot Chemical Facility
Energy resilience is critical at the Wintershall Dea Mittelplate offshore platform and its onshore counterpart Dieksand - Germany’s most productive oil field. Strict environmental and operational requirements leave little margin for error.
To reduce risk, Wintershall Dea introduced ABB’s OPTIMAX Energy Monitoring and Reporting system. Instead of relying on isolated datasets, the company gained a consolidated view of electricity, gas and oil flows across both facilities. Operators can now track efficiency trends, emissions and compliance with ISO 50001 in one place. The benefit is not just data visibility but greater operational certainty. Even as market conditions shift, the platform helps the company keep production reliable and safe.
And at Cabot GmbH’s Rheinfelden chemical facility, which produces fumed silica and alumina, transparency and efficiency were improved while easing regulatory reporting requirements by deploying OPTIMAX.
The system integrated multiple energy types, including electricity, natural gas and hydrogen, with process data from the plant. Managers now have AI-powered forecasting insights into energy use, enabling quicker decisions and compliance with ISO 50001 standards. By combining predictive analytics with continuous monitoring, Cabot strengthened both sustainability performance and day-to-day resilience in a highly energy-intensive environment.
Integration and safety across the value chain
Resilience is increasingly a system-wide, global concern. As industries participate in electricity and carbon markets, coordination across networks is a priority. Integrated platforms like OPTIMAX enable facilities to forecast demand and pricing with AI, align operations with market signals and run operations through closed-loop optimization.
This wider view provides new levers of stability. Facilities can avoid exposure to peak costs, balance pipeline pressures and improve reliability across entire clusters of industrial users.
Unplanned instability does not just cut into productivity. It can create unsafe conditions like pressure imbalances or gas leaks. Continuous predictive monitoring helps catch problems early, reducing these risks. Automated controls take this a step further, reducing the need for manual intervention in hazardous situations and freeing staff to focus on oversight and planning.
The road ahead
AI-enabled, closed-loop optimization and automated fault response can cut instability, extend asset life and strengthen resilience while lowering carbon intensity.
Those that move quickly will have an advantage. In an era of fast-changing markets and tightening regulations, companies that scale AI-driven energy management processes early will become best-in-class at keeping operations reliable when it matters most.
[1] https://www.iea.org/reports/energy-efficiency-policy-toolkit-2025/industry
[2] https://library.e.abb.com/public/2be1ed22350646c2bfc682da99276259/7PAA014956_OPTIMAX+for+Steam+%26+Power_Brochure.pdf?x-sign=7ZUPHPM5mHZQN1tvNtmlHPOocaPDnCYfb6%2BFoe%2BqfDazLQ0t19CvAHixO2SxV4d%2F
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ABB Limited (Zurich)
ABB Industrial Automation
Affolternstrasse 44
Zurich
8050
Switzerland
Tel: +65 (0)6773 585
Web: https://www.abb.com
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