Real-Time Data, Real Climate Impact: Optimizing Heat with Intelligence

Industrial manufacturing relies heavily on thermal energy – over 60% of the energy used by European industry is for heat (for more info check the article here: ​ehpa.org). Traditionally, this heat has come from burning fossil fuels, making industrial heat a major contributor to carbon emissions. As climate goals tighten, process engineers face the dual challenge of decarbonizing heat while maintaining efficiency and reliability. Industrial heat pumps have emerged as a key solution: they can deliver high-temperature heat (up to ~180–200 °C) using electricity and waste heat, covering an estimated 37% of industrial heat demand if fully deployed (for more info, check the article here: ​ehpa.org). Their appeal lies in exceptional efficiency – heat pumps move thermal energy instead of generating it from fuel, often achieving three to five times more heat output per unit of energy input than a boiler. This efficiency translates to big reductions in energy use, operating cost, and emissions for industrial sites (for more detail check the article here: ​wbcsd.org). Now, a new dimension is amplifying these benefits even further: real-time data. By intelligently controlling and optimizing heat pumps with live data and analytics, industries are unlocking even greater energy savings and climate impact.

The Efficiency Edge of Industrial Heat Pumps

The fundamental advantage of a heat pump is its Coefficient of Performance (COP) – a ratio that can easily exceed 3 or 4, meaning a heat pump can provide 3–4 units of heat for every 1 unit of electrical energy. Such high efficiency yields “significant reductions in overall energy use, operational costs and emissions” compared to conventional heating (for more info check the article here: ​wbcsd.org). Moreover, heat pumps enable circular energy solutions: they can capture and upgrade waste heat from one process to be reused in another, turning a by-product into a valuable resource (check the article here: ​wbcsd.org). In practice, this means an industrial heat pump can often replace or supplement a gas-fired boiler with a far lower carbon footprint. According to the International Energy Agency (IEA), heat pumps today cut greenhouse emissions by at least 20% compared to gas boilers even on a coal-intensive grid, and by up to 80% when powered by cleaner electricity (for more info check the article here: ​carbonbrief.org).

The climate impact is tangible. For example, a high-temperature heat pump installed in a food processing plant (375 kW capacity) was calculated to save about 3,000 MWh of energy per year, eliminating roughly 600 tons of CO₂ emissions annually by replacing a gas-fired system. These gains in efficiency and emissions reduction make a compelling case for electrifying industrial heat. However, simply installing a heat pump is only the beginning – how the system is operated and controlled over time determines whether its full potential is realized. This is where real-time data and intelligent optimization become crucial.

Data-Driven Thermal Optimization in Real Time

Modern industrial heat pumps are not just efficient; they are increasingly smart and connected. Equipped with sensors, IoT connectivity, and advanced control systems, these units can continuously feed performance data to cloud platforms or on-site control systems. According to an IEA Heat Pumping Technologies report, IoT-enabled heat pumps open up a variety of new possibilities: data can be used for “preventive analytics… predictive maintenance, fine-tuning of operation parameters and benchmarking”. In other words, a steady stream of real-time data allows engineers to optimize thermal operations dynamically rather than by static settings. Key capabilities include:

·       Fine-Tuned Control: Live data on temperatures, pressures, and flow rates enable real-time adjustments of heat pump set points, ensuring optimal efficiency by dynamically responding to evolving conditions like ambient temperature or process demand (for more detail, check the article here: ​heatpumpingtechnologies.org). There is no guarantee the commissioning settings remain optimal under all conditions; ongoing data-driven tweaks ensure the heat pump always runs at peak performance.

·       Predictive Maintenance: Data analytics detect early signs of wear, enabling timely service to prevent downtime and maintain efficiency.

·       Demand Response & Energy Management: Heat pumps adjust operations to align with cheaper, greener electricity, reducing costs and balancing grid demand.

·       Benchmarking and Continuous Improvement: Performance data allows comparison across systems, identifying best practices and driving efficiency improvements.

Underpinning these capabilities are emerging tools like digital twins – virtual replicas of physical systems that run in parallel to the real equipment. A digital twin of an industrial heat pump system can ingest real-time sensor data and simulate the system’s behavior instantly. This provides a powerful sandbox to test optimization strategies. Researchers note that digital twins make it possible to process vast data in real time, enabling “enhanced control and surveillance strategies” for heat pumps (for more info check the article here: ​heatpumpingtechnologies.org). In practice, engineers can use a digital twin to try out a new control strategy (like lowering a supply temperature set point) virtually and predict the outcome before applying it to the real system. The twin can adapt to changes in the physical equipment’s performance (like degradation), ensuring that control strategies remain optimal over time (for more information check the article here: ​heatpumpingtechnologies.org).

Intelligent Heat Pump Operations: Proof in Practice

Data-driven optimization of heat pumps is not just theory – real industrial projects are already reaping the benefits. A notable example comes from a district heating network in Vienna. There, a large heat pump was integrated into the network, and engineers employed a real-time digital twin platform to optimize its operation. By continuously adjusting the network’s water temperature and the heat pump’s settings, they were able to produce around 10% more heat with the same energy input (for more detail check for the explanation here: ​gradyent.ai). In effect, intelligent control unlocked an extra 10% efficiency gain simply through better coordination and set point management. This digital twin incorporated live data on weather, demand, and sensor readings, alongside physics-based models and AI, to find the sweet spot of operation (for more info check the article here: ​gradyent.ai). As a bonus, lowering network temperatures by just a few degrees through this optimization also decreased heat losses and CO₂ emissions across the system (for more info check the article here: ​gradyent.ai). Wien Energie, the utility, projected that scaling this approach to its 20 largest network segments could save up to 4,000 MWh of heat annually (for more info check the article here: ​gradyent.ai) – a testament to how small percentage improvements yield huge absolute savings in large-scale operations.

In the building sector, Fraunhofer ISE developed an AI-driven heat pump controller that adapts to real-time conditions, avoiding inefficiencies and achieving 5–13% energy savings in tests. This principle applies to industrial systems, where advanced algorithms continuously align operations with fluctuating loads and conditions for optimal performance. By combining smart controls and analytics with efficient hardware, industries can reduce energy use and CO₂ emissions significantly while enhancing financial and sustainability outcomes.

Climate and Operational Benefits of Data-Optimized Heat

Maximizing heat pump performance with data drives operational excellence and climate impact by reducing energy consumption, costs, maintenance needs, and emissions while improving system reliability, extending equipment life, and aligning stakeholders around measurable improvements.

On the climate side, the implications are significant. Every unit of energy saved through optimization is a unit of energy that doesn’t need to be generated. When that saved energy would have come from burning fossil fuels on site (or drawing additional power from a fossil-heavy grid), the CO₂ emissions avoided add up quickly. Real-time optimization also enables better use of renewable energy: a smart heat pump system can capitalize on periods of surplus green power, further cutting the carbon intensity of the heat it produces. The European Heat Pump Association emphasizes that deploying heat pumps in industry is essential for meeting decarbonization targets, as they directly cut emissions in energy-intensive processes while also reducing reliance on imported fossil fuels (for more info check here: ​ehpa.org​ehpa.org). In EU analyses, a broad adoption of industrial heat pumps and related measures could allow the bloc’s industry to eliminate roughly 25% of its CO₂ emissions while enhancing energy security (for more info check it here: ​uk.news.yahoo.com). Optimized heat pump operations accelerate this impact by ensuring each installed system delivers the maximum possible emission reduction.

It’s also worth noting that intelligent heat management helps industries participate in broader climate initiatives. For instance, by flattening peak demand, an optimized heat pump can reduce the need for peaking power plants (often gas-fired) on the grid, indirectly shrinking the grid’s carbon footprint. Likewise, data-driven efficiency gains can be counted toward corporate sustainability goals and carbon accounting. In summary, optimizing heat with intelligence yields a cascade of benefits – from kilowatts saved to tons of CO₂ abated – aligning operational performance with climate responsibility.

An engineer leverages Entropic’s real-time optimization platform to monitor and enhance the performance of an industrial heat pump system, driving efficiency and sustainability in manufacturing operations.

Why Entropic Does It Better

Entropic simplifies industrial heat optimization by integrating real-time data, intelligent simulations, and holistic analysis into a user-friendly platform. Its AI-powered tools enable engineers to simulate, optimize, and monitor heat systems effectively, aligning operational performance with sustainability goals. By automating complex analyses, Entropic accelerates decision-making and ensures systems remain efficient over time.

·      Real-time digital modeling of thermal systems to create physics-based digital models.

·      Automated simulations to identify optimal energy-saving strategies quickly.

·      Real-time monitoring for proactive maintenance and continuous efficiency improvement.

·      Data-driven projections of energy savings, emissions reductions, and financial outcomes.