Fouling Mitigation Strategies in Heat Exchanger Networks
Summary
Fouling in heat exchanger networks (HENs) arises from the deposition of particulates, scales and organic films on heat transfer surfaces, leading to increased thermal resistance, pressure drop and operational costs. Mitigation strategies span from design enhancements and predictive modelling to proactive cleaning and advanced surface treatments. Design interventions include optimised flow velocities, surface roughness modifications and the incorporation of turbulence promoters to reduce deposit adhesion. Predictive frameworks leverage semi-empirical and threshold fouling models, often integrated with computational fluid dynamics (CFD), to forecast fouling onset and growth under varying process conditions. Data-driven techniques, coupled with knowledge-based feature extraction, enable long-term monitoring and accurate forecasting of fouling severity, thereby informing maintenance schedules. Cleaning approaches range from mechanical pigging and chemical descaling to optimally scheduled offline cleaning, which can be refined using moving-window decision algorithms for complex networks. Surface coatings and anti-fouling materials, including hydrophobic and catalytic layers, present emerging routes to suppress deposit formation. Taken together, these strategies underscore a holistic approach—combining design, monitoring, modelling and targeted cleaning—to sustain energy efficiency, safety and reliability in industrial HENs globally.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Fouling Mitigation Strategies in Heat Exchanger Networks publication trend
The graph below shows the total number of articles in fouling mitigation strategies in heat exchanger networks across all publications each year (not limited to Nature Index journals).
Technical terms
Fouling: Accumulation of unwanted deposits on heat transfer surfaces, degrading performance.
Heat Exchanger Network (HEN): An interconnected assembly of heat exchangers designed to maximise energy recovery.
Threshold Fouling Model: A predictive model assuming fouling initiates abruptly once a process variable exceeds a defined limit.
Deterministic Fouling Model: A semi-empirical framework describing deposit growth as a continuous function of temperature, flow and composition.
Computational Fluid Dynamics (CFD): Numerical simulation technique used to analyse fluid flow and heat transfer, often applied to fouling studies.
Moving-Window Decision-Making Algorithm: An optimisation strategy that divides a long-term horizon into shifting intervals to solve complex scheduling problems efficiently.
References
- Hybrid Approach for Advanced Monitoring and Forecasting of Fouling with Application to an Ethylene Oxide Plant. Industrial & Engineering Chemistry Research (2024).
- Cleaning Schedule Optimization of Heat Exchanger Network Using Moving Window Decision-Making Algorithm. Applied Sciences (2023).
- Modeling Strategies for Crude Oil-Induced Fouling in Heat Exchangers: A Review. Processes (2023).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.