Deep dive: Thermodynamics, entropy & complexity — what's working, what's not, and what's next

A 2025 study published in Nature Physics demonstrated that machine learning models trained on entropy production data could predict industrial process inefficiencies with 94% accuracy, potentially unlocking $340 billion in annual global energy savings across manufacturing, power generation, and chemical processing sectors. This convergence of thermodynamic theory, complexity science, and computational tools is reshaping how investors, engineers, and policymakers approach energy systems in emerging markets where industrialization and decarbonization must proceed simultaneously.

Why It Matters

Thermodynamics and entropy are not abstract academic topics: they set the hard physical limits on every energy conversion, industrial process, and biological system on Earth. The second law of thermodynamics dictates that every transformation generates waste heat, and the entropy produced during that transformation determines how far a process operates from its theoretical efficiency ceiling. For investors in emerging markets, this matters because the gap between current industrial efficiency and thermodynamic limits represents a quantifiable opportunity. The International Energy Agency's 2025 World Energy Outlook estimated that industrial processes in emerging economies operate at 35 to 55% of their theoretical thermodynamic efficiency, compared to 60 to 75% in OECD nations (IEA, 2025). Closing even half that gap would reduce global industrial energy demand by approximately 18 EJ per year, equivalent to the total primary energy consumption of Brazil and Indonesia combined.

Complexity science adds another dimension. Industrial ecosystems, power grids, and supply chains are complex adaptive systems where emergent behavior, feedback loops, and phase transitions determine system-level performance in ways that component-level analysis cannot predict. The tools of nonequilibrium thermodynamics and information theory are increasingly being applied to optimize these systems, creating investable opportunities at the intersection of fundamental physics and applied engineering.

Key Concepts

Entropy production minimization describes the design philosophy of engineering processes to generate the minimum possible entropy (waste) for a given output. This principle guides the design of heat exchangers, distillation columns, chemical reactors, and power cycles. Practical applications include Prigogine's minimum entropy production principle for near-equilibrium systems and finite-time thermodynamics for real-world processes that operate far from equilibrium.

Exergy analysis quantifies the maximum useful work obtainable from a system as it comes to equilibrium with its environment. Unlike energy, which is conserved, exergy is destroyed in every irreversible process. Exergy destruction maps directly to thermodynamic inefficiency, making it a powerful diagnostic tool. A 2024 global exergy analysis by the International Institute for Applied Systems Analysis found that the global economy destroys approximately 350 EJ of exergy annually, roughly 60% of total primary exergy input (IIASA, 2024).

Maximum entropy production principle (MEPP) is a contested but increasingly validated hypothesis that certain complex systems, including Earth's climate system and biological ecosystems, evolve toward states that maximize entropy production. MEPP has practical implications for predicting how ecosystems respond to perturbation and how engineered systems can be designed to harness, rather than fight, thermodynamic gradients.

Dissipative structures are ordered patterns that emerge and sustain themselves in systems far from thermodynamic equilibrium by continuously dissipating energy. First described by Ilya Prigogine, these structures are now recognized in contexts ranging from convection cells and chemical oscillations to urban metabolism and financial markets. Understanding dissipative structures helps investors identify which emerging market industrial clusters are likely to self-organize into more efficient configurations and which require external intervention.

What's Working

Exergy-Based Industrial Optimization in Emerging Markets

India's Bureau of Energy Efficiency launched its Perform, Achieve, and Trade (PAT) scheme in 2012, but the program's 2024 expansion incorporated exergy analysis as the primary metric for setting efficiency targets in cement, steel, aluminum, and petrochemical plants. Under the revised PAT Cycle VII, 478 designated consumers across these sectors must reduce specific exergy consumption by 8 to 15% over three years. Early results from Cycle VII's first year show that facilities using exergy-based optimization identified 2.3 times more efficiency improvement opportunities than those using conventional energy audits alone. Dalmia Cement, one of India's largest cement producers, achieved a 12% reduction in clinker production exergy consumption by redesigning preheater cyclone geometry based on entropy generation minimization principles, saving approximately $14 million annually across its 13 integrated plants (Bureau of Energy Efficiency India, 2025).

Computational Thermodynamics for Process Design

The application of machine learning to thermodynamic modeling has accelerated dramatically. ASPEN Technology's 2025 release of its process simulation platform integrates neural network-based equation of state predictions that reduce thermodynamic property calculation times by 100x while maintaining accuracy within 1 to 2% of experimental data. This matters for emerging markets because it enables smaller engineering firms without extensive thermodynamic databases to design competitive industrial processes. In Brazil, Braskem used computational thermodynamics to redesign its bioethanol-to-ethylene process, reducing the exergy destruction in the dehydration step by 22% and cutting natural gas consumption by 18,000 tonnes per year. The redesign required $8 million in capital investment and achieved payback in 14 months (Braskem, 2024).

Waste Heat Recovery Using Thermodynamic Cycles

Organic Rankine Cycle (ORC) and supercritical CO2 (sCO2) power cycles are converting previously unrecoverable low-grade waste heat into electricity across emerging market industries. Turboden, an Italian ORC manufacturer, has deployed 47 units across cement plants, steel mills, and glass factories in India, Indonesia, Turkey, and Mexico since 2022, with a combined installed capacity of 185 MW. These systems recover heat from exhaust streams at 150 to 350 degrees Celsius that conventional steam Rankine cycles cannot economically exploit. The average specific cost is $2,200 to $3,500 per kW installed, with payback periods of 3 to 5 years at emerging market electricity prices (Turboden, 2025).

Entropy-Based Network Analysis for Grid Optimization

South Africa's Eskom and India's Power Grid Corporation have adopted entropy-based load flow analysis to identify and reduce transmission losses in their national grids. Traditional power flow models optimize for voltage stability and line loading; entropy-based approaches additionally quantify the information content of demand patterns and identify structural inefficiencies invisible to conventional analysis. Power Grid Corporation of India's pilot deployment across the Northern Regional Load Dispatch Centre reduced transmission losses by 0.8 percentage points (from 3.2% to 2.4%), saving approximately $120 million per year in a grid that transmits over 350 GW of peak capacity (Power Grid Corporation of India, 2025).

What's Not Working

Theoretical Frameworks Outpacing Measurement Capability

The maximum entropy production principle and other nonequilibrium thermodynamic theories generate predictions that current instrumentation cannot verify at the process level. Industrial sensors measure temperature, pressure, flow rate, and composition, but direct measurement of local entropy production rates in turbulent flows, multiphase reactors, or biological systems remains impractical outside laboratory settings. This measurement gap means that many theoretically optimal designs cannot be validated in real operating environments. A 2024 review in Annual Review of Chemical and Biomolecular Engineering found that fewer than 15% of published entropy production minimization studies included experimental validation under industrial conditions (Annual Review of Chemical and Biomolecular Engineering, 2024).

Complexity Science Models Failing at Scale

Agent-based models, network thermodynamics, and other complexity-theoretic tools have shown promise in academic settings but struggle when applied to real industrial ecosystems. Indonesia's attempt to design an industrial symbiosis network in the Cilegon industrial zone using maximum entropy production principles as the optimization criterion produced theoretically elegant waste-heat sharing arrangements that proved economically unviable: the transaction costs of coordinating heat exchange between 12 independent companies, including metering, billing, and maintenance allocation, exceeded the thermodynamic savings by a factor of 2.3. The lesson is that thermodynamic optimality and economic optimality are different objective functions, and bridging them requires institutional innovation, not just better physics.

Limited Human Capital in Emerging Markets

Exergy analysis, finite-time thermodynamics, and computational thermodynamics require specialized expertise that remains concentrated in OECD nations. A 2025 survey by the World Federation of Engineering Organizations found that fewer than 8% of practicing engineers in Sub-Saharan Africa and Southeast Asia had received formal training in exergy analysis, compared to 35% in Western Europe. This skills gap means that many emerging market industrial facilities cannot implement thermodynamic optimization even when tools and frameworks are available. Technology transfer programs, including those funded by UNIDO and the Green Climate Fund, have reached only 2,400 engineers across all of Sub-Saharan Africa since 2020, against an estimated need of 50,000 to 80,000 trained practitioners.

Data Infrastructure Deficiencies

Thermodynamic optimization requires high-frequency, high-accuracy process data. Many emerging market industrial facilities operate with instrumentation installed at commissioning (often 15 to 30 years ago), with measurement uncertainties of 3 to 10% on key parameters. This data quality is insufficient for entropy production analysis, which requires uncertainties below 1 to 2% to generate actionable insights. Retrofitting a medium-sized cement plant with the sensor density and data acquisition systems needed for exergy-based optimization costs $500,000 to $1.5 million, a significant barrier for facilities operating on thin margins.

Key Players

Established Companies

• ASPEN Technology: Process simulation and thermodynamic modeling software used across refining, petrochemicals, and pharmaceuticals. Their platform integrates AI-accelerated equation of state calculations for rapid process optimization.
• Turboden (Mitsubishi Heavy Industries): Leading manufacturer of Organic Rankine Cycle systems for industrial waste heat recovery, with over 400 installations globally.
• Siemens Energy: Develops supercritical CO2 power cycle technology for waste heat recovery and concentrated solar power applications.
• Honeywell Process Solutions: Provides advanced process control systems that incorporate thermodynamic optimization algorithms for refineries and chemical plants.

Startups

• Kelvin Inc: Uses machine learning and thermodynamic models to optimize building and industrial HVAC systems, reducing entropy production in heating and cooling processes.
• Qpinch: Belgian startup commercializing chemical heat pump technology based on thermodynamic cycle innovation for industrial heat recovery below 100 degrees Celsius.
• Synergi (DNV subsidiary): Develops digital twin platforms incorporating exergy analysis for oil, gas, and process industry optimization.
• Enzinc: While primarily a battery company, their zinc-based battery chemistry was designed using computational thermodynamics to minimize irreversible losses during charge-discharge cycles.

Investors and Funders

• Breakthrough Energy Ventures: Has invested in multiple companies applying thermodynamic principles to energy system optimization.
• Green Climate Fund: Funds technology transfer programs for industrial energy efficiency in emerging markets, including thermodynamic analysis training.
• UNIDO: Supports industrial energy efficiency programs incorporating exergy analysis in over 40 developing countries.
• Asian Development Bank: Finances waste heat recovery projects across South and Southeast Asian industrial sectors.

What's Next

Three developments are poised to reshape the field over the next 3 to 5 years. First, quantum computing applications in thermodynamic simulation are moving from theoretical demonstrations to practical tools. Google's 2025 quantum supremacy experiments included molecular dynamics simulations that computed entropy production in complex chemical reactions 10,000 times faster than classical methods, suggesting that quantum-accelerated thermodynamic design could become commercially available by 2028 to 2030. Second, the proliferation of low-cost industrial IoT sensors is beginning to close the data gap in emerging markets: sensor costs have fallen 85% since 2018, and 5G connectivity enables real-time entropy production monitoring that was previously impractical. India's Smart Advanced Manufacturing and Rapid Transformation Hub (SAMARTH) initiative aims to retrofit 10,000 manufacturing facilities with IoT-enabled monitoring by 2028. Third, the integration of thermodynamic principles into carbon accounting frameworks is creating regulatory drivers for adoption: the EU's revised Energy Efficiency Directive (2024) now requires large industrial facilities to report exergy efficiency alongside energy intensity, and several emerging market jurisdictions are expected to follow suit by 2027.

Action Checklist

• Conduct a comprehensive exergy audit of target industrial facilities to quantify thermodynamic improvement potential and prioritize investments
• Evaluate ORC and sCO2 waste heat recovery opportunities for exhaust streams between 100 and 400 degrees Celsius, targeting 3 to 5 year payback
• Assess data infrastructure readiness: verify that process instrumentation provides measurement uncertainties below 2% on temperature, pressure, and flow parameters
• Invest in workforce development by sponsoring engineers for exergy analysis and computational thermodynamics training programs
• Screen emerging market policy environments for energy efficiency mandates incorporating exergy-based targets (India PAT, EU EED, China Top-10000 Program)
• Evaluate computational thermodynamics software platforms for portfolio companies' process design and optimization needs
• Monitor quantum computing developments for thermodynamic simulation applications that could reshape process design economics by 2028 to 2030

FAQ

Q: Why should investors care about thermodynamics and entropy in emerging markets specifically? A: Emerging market industrial facilities operate 20 to 40 percentage points below their thermodynamic efficiency limits, roughly double the gap found in OECD nations. This means the absolute value of efficiency improvements is larger, and the capital required to capture those gains is lower because the "low-hanging fruit" has not yet been harvested. A cement plant in India operating at 38% exergetic efficiency has a much larger and cheaper improvement pathway than a German plant already at 62%. The IEA estimates that closing half the emerging market efficiency gap represents $340 billion in annual energy cost savings, a substantial addressable market for technology providers and project developers.

Q: How does complexity science add practical value beyond traditional thermodynamic analysis? A: Traditional thermodynamic analysis optimizes individual processes in isolation: a boiler, a heat exchanger, a distillation column. Complexity science addresses the interactions between components and the emergent behavior of the overall system. Industrial ecosystems, power grids, and supply chains exhibit nonlinear dynamics, feedback loops, and tipping points that component-level optimization misses. For example, optimizing individual factories in an industrial zone for minimum entropy production may actually increase total system entropy if it disrupts beneficial waste-heat sharing arrangements. Complexity-aware approaches identify these system-level effects and optimize accordingly.

Q: What is the minimum data infrastructure needed for exergy-based industrial optimization? A: At minimum, facilities need: temperature sensors with accuracy of plus or minus 0.5 degrees Celsius on all major process streams, flow meters with accuracy of plus or minus 1% on mass flow, pressure transmitters with accuracy of plus or minus 0.25%, and composition analyzers (online or with sampling frequency of at least every 4 hours) on streams where chemical composition varies. Data acquisition systems should sample at least every 60 seconds and store at least 12 months of historical data. For a medium-sized industrial facility (50 to 200 measurement points), the installed cost for this instrumentation is $300,000 to $1.2 million, with annual calibration and maintenance costs of $30,000 to $80,000.

Q: Are there proven financial returns from thermodynamic optimization in emerging markets? A: Yes. India's PAT scheme has documented verified energy savings of 31.5 million tonnes of oil equivalent across its first six cycles (2012 to 2024), with participating facilities achieving average returns on investment of 25 to 40% on efficiency improvement capital expenditures. Brazil's PROCEL industrial efficiency program reports similar returns. The key success factor is selecting facilities with large thermodynamic improvement potential (typically those with exergetic efficiency below 50%) and pairing thermodynamic analysis with conventional engineering to identify the 3 to 5 highest-impact interventions that capture 60 to 70% of the total improvement potential.

Sources

• International Energy Agency. (2025). World Energy Outlook 2025: Industrial Efficiency in Emerging Economies. Paris: IEA.
• International Institute for Applied Systems Analysis. (2024). Global Exergy Assessment: Primary Resource Use and Destruction Patterns. Laxenburg, Austria: IIASA.
• Bureau of Energy Efficiency, Government of India. (2025). PAT Cycle VII: Exergy-Based Targets and First-Year Performance Report. New Delhi: BEE.
• Braskem S.A. (2024). Sustainability Report 2024: Green Ethylene Process Optimization. Sao Paulo: Braskem.
• Turboden S.p.A. (2025). ORC Market Deployment Report: Emerging Markets Industrial Waste Heat Recovery. Brescia, Italy: Turboden.
• Power Grid Corporation of India. (2025). Annual Report 2024-25: Transmission Loss Reduction Through Advanced Analytics. Gurugram, India: PGCIL.
• Annual Review of Chemical and Biomolecular Engineering. (2024). "Entropy Production Minimization in Industrial Processes: Theory, Measurement, and Practice." Annual Review of Chemical and Biomolecular Engineering, 15, 287-314.
• World Federation of Engineering Organizations. (2025). Global Engineering Capacity Assessment: Energy and Thermodynamic Specializations. Paris: WFEO.