Refactoring for Better Code Reusability in Chemical Process Engineering Software

In the field of chemical process engineering, software tools are essential for designing, analyzing, and optimizing complex chemical processes. As these tools evolve, maintaining clean and reusable code becomes increasingly important to ensure efficiency, scalability, and ease of updates. Chemical process engineers rely on software ranging from steady-state simulators (like Aspen Plus) to dynamic modeling environments (like gPROMS) and custom-built computational fluid dynamics (CFD) solvers. Each of these systems handles thousands of lines of code dealing with thermodynamic correlations, unit operation models, numerical solvers, and data handling. Without deliberate refactoring, the codebase quickly accumulates technical debt, making future enhancements time-consuming and error-prone. This article explores the principles, strategies, and real-world benefits of refactoring to improve code reusability in this demanding domain.

Why Code Reusability Matters in Chemical Engineering Software

Chemical process engineering software is uniquely challenging because it must integrate physics-based models with numerical methods, large datasets, and user interfaces. Reusing code components across different projects—or even within the same project—saves development time and reduces the risk of introducing bugs into validated models. For example, a property calculation routine for water‑steam tables can be used in a boiler model, a steam turbine, and a flash separator. If that routine is written once and reused, any correction or update propagates automatically, ensuring consistency across simulations.

Beyond efficiency, reusability improves collaboration across teams. When code is modular and well-defined, a junior developer can add a new reactor model without needing to understand every detail of the thermodynamic engine. This accelerates onboarding and allows specialists (e.g., in distillation or reaction kinetics) to focus on their domain rather than on boilerplate infrastructure. In regulated industries, reuse also supports traceability and validation—fewer code paths to audit means lower compliance overhead.

Common Challenges in Legacy Chemical Engineering Codebases

Before diving into refactoring techniques, it helps to recognize the typical pain points found in older or rapidly grown chemical engineering software:

  • Monolithic architectures: A single file may contain UI code, computational logic, and database access. Any change risks breaking unrelated functionality.
  • Duplicated logic: Repeat calculations for Antoine coefficients, SRK equation‑of‑state parameters, or heat integration appear in multiple places. Fixing an error in one location often leaves others untouched.
  • Hard‑coded constants: Physical constants, convergence tolerances, or component properties are sprinkled throughout the code, making maintenance a detective exercise.
  • Poor naming and lack of tests: Variables like a34 or functions named calc_it() obscure intent. Without a test suite, refactoring feels like walking a tightrope without a net.

These issues are not unique to chemical engineering, but the high cost of errors (e.g., predicting an unsafe reaction) makes them especially critical to address through systematic refactoring.

Fundamental Refactoring Strategies for Reusability

Identify and Extract Reusable Components

Begin by scanning the codebase for patterns that repeat. Look for functions or classes that perform a single, well‑defined task—for instance, converting units, calculating liquid‑vapor equilibrium (VLE) using Raoult’s law, or performing a basic Newton‑Raphson iteration. Extract these into their own modules or utility classes. In Python, this means creating a thermoUtils.py or numeric.py module; in C++ you might build a namespace ChemicalUtil.

When extracting, ensure the component is independent: it should not rely on global variables or the main application context. Pass all required parameters explicitly. This makes the component testable in isolation and reusable in any future project that needs the same calculation. For example, a routine that computes vapor pressure from the Antoine equation should take temperature and the three Antoine coefficients as inputs, not read them from a global species database.

Apply the Single Responsibility Principle (SRP)

Each module or class should have one reason to change. In chemical software, it’s common to see a “flash calculator” class that also handles data logging and plotting. Refactor by separating concerns: one class for the thermodynamic flash algorithm, another for logging results, and a third for visualization. The flash calculator can then be reused in a distillation column model or a batch reactor simulation without dragging along logging dependencies.

Use Design Patterns That Promote Reuse

  • Factory Pattern: Use a factory to instantiate different property methods (e.g., PR, SRK, or Peng‑Robinson equations) based on configuration. This prevents the code from being cluttered with conditional statements and makes it easy to add new models.
  • Strategy Pattern: Encapsulate varying algorithms—like different convergence criteria for a flowsheet solver—behind a common interface. The calling code remains unchanged, and new strategies can be added independently.
  • Observer Pattern: Useful for triggering notifications when simulation variables change (e.g., for real‑time monitoring dashboards). This decouples the simulation engine from the UI, allowing the engine to be reused in batch‑mode or script‑driven scenarios.

Applying these patterns not only encourages reuse but also makes the code more flexible for future requirements, such as swapping a simple equilibrium model for a rate‑based one.

Refactor Data Access and Configuration

Chemical software often reads large thermodynamic databases or reaction rate parameters. Hard‑coding these data is a direct enemy of reusability. Instead, refactor to use external configuration files (JSON, YAML, or SQLite databases). The same codebase can then be reused for different chemicals or processes simply by swapping configuration files. This approach also enables version control of data separately from code, simplifying updates when new experimental data becomes available.

For example, a reactor kinetics module that reads rate constants and activation energies from a YAML file can be reused across multiple projects—fine chemicals, petrochemicals, or pharmaceuticals—by providing different YAML configurations. No code changes are needed.

Case Study: Refactoring a VLE Calculation Module

Consider a legacy Fortran or Python module that performs a bubble‑point calculation. The original code might be 500 lines long, interleaving the Van Laar activity coefficient model with the Antoine vapor pressure routine and a bisection solver, all inside a single function. The function is called from ten different places, each repeating similar logic for different components.

To refactor, we break it into three reusable components:

  1. ActivityCoefficient: A class hierarchy with subclasses for Wilson, NRTL, UNIQUAC, etc. Each subclass implements a standard interface (computeGamma(x, T)).
  2. VaporPressure: A class that takes Antoine coefficients and returns P_sat at a given temperature. It can be replaced with a Wagner‑type equation in the future.
  3. BubblePointSolver: A generic solver that accepts any activity model and vapor pressure calculator, then performs the iterative calculation until convergence.

Now the bubble‑point calculation can be reused for any mixture by plugging in the appropriate models. Testing each component separately becomes trivial: you can unit‑test the NRTL model without running the full flash. The new code is about half the original size, with clear responsibilities. Moreover, a new engineer can implement a PSRK equation of state by writing just the new model class, reusing the solver and vapor pressure calculator.

Testing as a Safety Net for Refactoring

Refactoring without tests is risky, especially in chemical engineering where a small numerical error can produce a drastically wrong result. Before making any changes, ensure the existing code has a test suite that covers expected outputs. If tests don’t exist, write them first—this process is sometimes called “characterizing” the code. Use known benchmark values: bubble point of a benzene‑toluene mixture at 1 atm, heat of reaction for a combustion process, etc.

After extracting a reusable component, verify that the test suite still passes. Then, add new tests that test the component in isolation. For example, test that the NRTL activity coefficient for an ethanol‑water system matches literature values at various compositions. This approach ensures that refactoring improves reusability without sacrificing accuracy—a non‑negotiable requirement in process engineering.

Tools and Techniques to Support Refactoring

Modern IDEs (PyCharm, Visual Studio, Eclipse) provide automated refactoring tools such as “Extract Method,” “Extract Variable,” and “Rename Symbol.” Use them with caution—always review the diff. Additionally, version control (Git) is essential: commit before each refactoring step so you can backtrack if needed.

Static analysis tools like pylint or flake8 can flag duplicated code and long functions. For complex dependency analysis, tools like Understand or SourceMeter visualize the call graph, helping you identify the most tangled sections to refactor first.

In the context of chemical process engineering, consider using unit testing frameworks (pytest, JUnit) with parameterized tests to validate numerical accuracy. Continuous integration (CI) pipelines should run the full test suite on every commit, catching regressions early.

Benefits Realized Through Refactoring

Companies that have invested in systematic refactoring report measurable improvements:

  • Reduced time to market: A new reactor model can be assembled from existing components in weeks instead of months. A pharmaceutical company cut the development of a batch reactor simulation package by 40% after refactoring their thermodynamic library into reusable modules.
  • Lower bug incidence: When fixing a bug in a Unity‑type activity coefficient model, the correction propagates to all 20 unit operations that use it. Formal code reviews of the smaller, focused modules are also more effective.
  • Easier knowledge transfer: New hires can understand a modular codebase faster. One chemical‑engineering software vendor reported that onboarding time for new developers dropped from three months to six weeks after a major refactoring effort.
  • Support for parallel development: Teams can work on different reusable components concurrently. For instance, one group improves the numerical solver while another adds a new property method—without merge conflicts.

Overcoming Resistance to Refactoring

Despite the clear benefits, refactoring can be met with skepticism, especially in engineering organizations that prioritize feature delivery over code quality. To build a case, start with a small, high‑impact refactoring—for example, extracting a unit conversion utility used in dozens of places. Measure the before‑and‑after: lines of code saved, test coverage gained, or time saved when adding a new unit operation. Share these metrics with stakeholders.

Also, emphasize that refactoring is not a one‑time cleanup but a continuous practice. Allocate a percentage of each sprint (e.g., 20%) to code improvements. This prevents the codebase from degrading again, ensuring that the initial investment in reusability pays off over the long term.

External Resources for Deeper Learning

To implement the strategies discussed, refer to these authoritative sources:

  • Refactoring: Improving the Design of Existing Code by Martin Fowler – The classic guide with step‑by‑step techniques, including many that apply directly to scientific software.
  • Design Patterns: Elements of Reusable Object‑Oriented Software by Gamma, Helm, Johnson, and Vlissides – The foundation for patterns like Factory and Strategy, widely adaptable to chemical simulation architectures.
  • Code Reuse in Process Engineering Software – A practitioner’s blog post at REACT Energy discussing practical reuse patterns in flowsheet simulators (external link).
  • Software Best Practices for Chemical Engineers – AIChE Chemical Engineering Progress article covering testing, refactoring, and modular design (external link).
  • Workflows of Refactoring – Martin Fowler’s article on how to integrate refactoring into daily development work (external link).

Conclusion

Refactoring for better code reusability transforms chemical process engineering software from a tangled collection of scripts into a well‑organized ecosystem of independent, tested components. By identifying reusable logic, applying modular design, adopting design patterns, and securing quality with automated tests, development teams can build software that is faster to extend, easier to maintain, and more reliable. The upfront effort pays for itself many times over through reduced development time, fewer errors, and smoother collaboration. In an industry where simulation accuracy directly impacts safety and profitability, clean, reusable code is not a luxury—it is a competitive necessity.