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Design
Best practice:
• Use segmented modelling when temperature differences are significant
• Explicitly evaluate local extreme values( temperature, velocity, ∆p)
• Make the outputs useful for operations teams, not just the design team
• Only use bulk-average methods when their limitations are understood and acceptable
From concept to reality: high-performance heat exchangers ready for integration
just moves the cost downstream. If thousands of variants can be evaluated computationally, it’ s hard to justify making a major equipment decision based on two or three familiar options. Proper optimization isn’ t an engineering luxury; it’ s a sound business decision. Best practice:
• Make optimization an explicit project deliverable, not an optional step
• Be clear about the trade-offs: CAPEX, OPEX, footprint, flexibility
• Run variant comparisons in a structured, repeatable way
• Present the results in a format that works for procurement and management, not just engineers
Mistake 5: Calculating at too coarse a resolution Professor perspective: Traditional design methods often use a single average value for fluid properties – heat capacity, density, viscosity, heat transfer coefficient – across the entire unit. The problem is that these properties can change significantly with temperature, and a single average misses that variation. This is fine for simple cases, but introduces real errors when temperature spans are large or fluid behaviour is strongly nonlinear. A better approach is to divide the heat exchanger into segments and recalculate fluid properties at each step. This gives a much more accurate picture of what’ s happening locally – both for heat transfer and pressure drop – and captures effects that a bulk-average model simply can’ t see. CEO perspective: Local effects are exactly what cause operational problems: fouling in specific zones, unexpected pressure drops, unstable control behaviour. A coarse model might get the overall performance roughly right, but it won’ t tell you where inside the unit things are going wrong. That’ s the information you need for material selection, maintenance planning, and day-to-day operation.
Mistake 6: Lack of comparability between offers What happens: Two bids come in at similar prices but are built on different assumptions – different fouling factors, different fluid properties, different minimum temperature approaches, different pressure drop limits, or different interpretations of the applicable standards. CEO perspective: When procurement asks“ which bid is better?”, the honest answer is often“ we can’ t tell, because we’ re not comparing like for like.” The decision then comes down to price or lead time – and the real cost appears later as change orders or performance disputes. Professor perspective: Meaningful comparison requires transparency. In research, disclosing your assumptions is standard practice. In industry, vendors naturally protect their methods – but it’ s still possible to define a minimum set of information that every bidder must provide to make comparison feasible. Best practice: Use a“ Bid Comparison Template” with mandatory fields: load cases used, material property sources, fouling assumptions, Δp definition, guaranteed performance, tolerances, standards basis( e. g., API 660 for shell-and-tube heat exchangers or API 661 for air-cooled heat exchangers in certain industries), and the scope of inspection and documentation.
Mistake 7: Scattered data and manual re-entry What happens: Specifications migrate between Excel spreadsheets, PDFs, emails, and handwritten notes. Data is transferred multiple times, versions become mixed, and the question“ Which number is valid?” costs more time than the actual design work. CEO perspective: In reality, this means: two weeks before the order is placed, someone finds a“ newer” spreadsheet. Or a supplier has calculated based on an outdated data sheet. Or the wrong unit( kg / h instead of t / h) slips in. This isn’ t unusual. It happens on most projects, and it’ s entirely avoidable. Professor perspective: Scattered data is a symptom of missing data structure. Without a single, versioned, machine-readable source of truth, automated checks and traceability are impossible. In complex plant environments, that’ s not just an inconvenience – it’ s a real project risk. Best practice: Maintain a single digital specification with clear version control, defined access roles, a full
18 Heat Exchanger World May 2026 www. heat-exchanger-world. com