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HEAT EXCHANGER NETWORK SYNTHESIS

HEAT EXCHANGER NETWORK SYNTHESIS banner
Live online Intermediate

HEAT EXCHANGER NETWORK SYNTHESIS

4(14)
97 views
₹ 3999
8 hrs
Next month
English
97 views
Enggenious (SAN Techno Mentors)
Enggenious (SAN Techno Mentors)
  • 7-day money-back guarantee
  • Session recordings included
  • Certificate of completion

Why enroll

  • Learn the techniques of heat exchanger network synthesis

  • Learn principles of optimization of processes by energy and resource analysis.

  • Learn how utility cost can be minimized

Is this course for you?

You should take this if

  • You work in Energy & Utilities or Nuclear & Power
  • You're a Chemical & Process / Mechanical Engineering professional
  • You have some foundational knowledge in the subject
  • You want to build skills in Corrosion, Energy efficiency optimization

You should skip if

  • You're looking for an introductory overview course
  • You need a different specialisation outside Chemical & Process
  • You need fully self-paced, on-demand content

Course details

Improving performance of a chemicals manufacturing facility is an ongoing process that must continue throughout the life of the plant.

Efficient use of heat can reduce the energy consumption which in turn reduces manufacturing costs. Networking of heat exchangers is a technique by which use of external utilities like, steam, cooling water, hot oil, chilled water, chilled brine etc., can be minimized. This can be achieved by exchanging heats of hot/cold of process streams with each other.

Network design methods are simple to understand and implement. Their main advantage is that they can be deployed prior to equipment design stage using only the process stream data. These methods can also be used in existing running plants under certain operating constraints. The network design provides optimum numbers and locations of heat exchangers within the processing train.

While designing the network, the optimization criteria used could be - minimization of external energy usage, minimization of heat transfer area or minimization of total annual cost.

This course also presents an example of optimization of number of effects of a multiple effect evaporator using the principles of optimization of processes by energy and resource analysis.

While the course presumes that the attendees possess Chemical Engineering background, other engineers from Process industries would also find the course useful.

Course suitable for

Key topics covered

Module 1 - Introduction, pinch technology, problem table algorithm, calculation of target area of heat exchanger networks

Module 2 - Countercurrent and mixed flow targets, cost targeting, rapid Sizing of heat exchangers, principles of optimization of Processes by energy and resource analysis

Opportunities that await you!

Skills & tools you'll gain

CorrosionEnergy efficiency optimizationEngineering & DesignGD&TPiping Layout

Career opportunities

Training details

This is a live course that has a scheduled start date.

Why people choose EveryEng

Industry-aligned courses, expert training, hands-on learning, recognized certifications, and job opportunities-all in a flexible and supportive environment.

What learners say about this course

Ved Naik
Ved Naik Engineering
May 3, 2026

This mapped pretty closely to the kind of PRs I’m skimming between standups, just framed around physical equipment instead of code. The intermediate level felt right; it assumes you know the basics and jumps into how maintenance decisions play out in prod-like conditions. The bit that stuck was the section on condition-based maintenance, specifically the example where a bearing’s vibration trend crosses the alert threshold but temp stays flat, and how they decide not to intervene yet. some of the early safety refreshers were a bit slow if you’ve worked around equipment before. Still, tying failure modes back to monitoring and obs habits made it easy to relate to infra work and energy utilities contexts. I wasn’t sold on the checklist format in Chapter 2, but the later edge cases around false positives and deferred fixes are where it separates itself.

ANU VARGHESE
ANU VARGHESE Fresher
Feb 25, 2026

Initially, I wasn’t sure what to expect from this course. The material stayed fairly grounded, especially when walking through open-loop versus closed-loop control beyond the textbook definitions. Examples tied well to things seen in chemical and pharmaceutical plants, like temperature control on a batch reactor and level control on a distillation column, rather than abstract blocks alone. There was also enough overlap with oil & gas and energy utilities to be useful, such as discussing pressure control on separators and basic boiler control logic. One challenge was mentally translating the simplified examples to real systems with dead time, sensor drift, and valve stiction. That gap is where junior engineers usually struggle, and it would have helped to explicitly call out those edge cases earlier. Still, the discussion on why open-loop control occasionally makes sense (maintenance modes, analyzer-based control) matched actual industry practice better than most courses. A practical takeaway was being more systematic about identifying the true process variable and disturbance before defaulting to a PID loop. Thinking at the system level—how one loop affects upstream and downstream units—was reinforced throughout. The content felt aligned with practical engineering demands.

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Muhammad Hussain
Feb 25, 2026

Initially, I wasn’t sure what to expect from this course. Process control is something that shows up everywhere on site, but the theory behind it had always been a bit fragmented for me. The sections on open-loop vs. closed-loop control helped close that gap, especially when tied to real examples like distillation column temperature control in chemical/pharmaceutical plants and boiler drum level control in energy utilities. One area that stood out was how feedback control behaves under disturbances. That directly connects to issues seen on an oil & gas separator pressure loop I’ve worked on, where load changes kept throwing the controller off. A challenge during the course was translating the block diagrams into what actually happens in the DCS screens, especially when multiple control objectives conflict. It took a bit of effort to map theory to noisy plant data. A practical takeaway was learning a more structured way to decide whether a loop even needs tight closed-loop control or if a simpler approach is acceptable. That alone will save time during commissioning and troubleshooting. The content feels immediately usable, and I can see this being useful in long-term project work.

Tarun Kumar Rajak
Tarun Kumar Rajak Piping Engineer
Feb 25, 2026

This course turned out to be more technical than I anticipated. The treatment of open- and closed-loop control went beyond block diagrams and actually tied into situations seen in chemical and oil & gas facilities. Examples around distillation column temperature control and refinery feed flow control felt familiar, especially when discussing interactions between loops rather than treating them in isolation. One challenge was translating the clean theoretical models into messy plant realities. Dead time, sensor drift, and valve stiction were touched on, but it still took effort to mentally map those concepts to something like boiler drum level control in energy utilities, where safety margins dominate tuning decisions. That gap is real in industry, and it showed up here. What worked well was the emphasis on understanding process behavior before jumping to controllers. A practical takeaway was the reminder to question whether a loop even needs to be closed, particularly for slow-moving pharmaceutical batch processes where manual intervention can be more robust. Compared with common industry practices, the course leaned more analytical than procedural, which is useful for system-level thinking. The content felt aligned with practical engineering demands.

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Questions and Answers

A: Choosing A risks validating thermal performance on a configuration that may already violate the pinch intent due to misrouted lines. Choosing B can force heat across the pinch during warm-up and mask a permanent routing error. Choosing D gives you good data on a fundamentally wrong network topology. Choosing C prevents irreversible heat integration errors before any thermal assumptions are tested.

A: Choosing A assumes linear LMTD behavior and ignores pinch-limited matches. Choosing B confuses energy recovery targets with exchanger sizing drivers. Choosing D exaggerates the penalty and doesn't match typical counter-current exchanger physics. Choosing C reflects the reduced driving force concentration around the pinch where most area accumulates.

A: Choosing A usually shifts totals but doesn't create a structural HEN error. Choosing B introduces minor numerical noise without changing match feasibility. Choosing C is within typical targeting tolerance and rarely changes topology. Choosing D indicates a fundamental violation that invalidates the entire synthesis regardless of numbers.

A: Choosing A hides a physical measurement problem and feeds bad data into the network. Choosing B assumes a failure mode without evidence and can bias heat recovery. Choosing D forces process behavior to match faulty instrumentation. Choosing C checks that the temperature basis for the pinch is even real.