You’ve built a solid simulation and now you’re working to “optimize” it. What does that mean in real terms? Should you reduce energy use, increase throughput, improve product purity—or all three? And how do you balance those goals without breaking constraints like safety limits, equipment specs, or regulatory requirements?
Engineers may try to optimize designs manually by tweaking variables, running sensitivity studies, and hoping for a better result. But this trial-and-error approach is time-consuming and often misses the best solution. What’s needed is a structured way to define objectives, apply constraints, and explore trade-offs efficiently. CHEMCAD’s optimization tools are designed to support exactly this kind of real-world problem solving.
CHEMCAD includes a built-in optimization solver accessible from the Analysis tab under the Optimization group.
To start a new optimization study, click Create New Optimization, then enter a name and click OK to bring up the Process Optimization dialog. After clicking Define Objective Function, make a selection in the New Objective Function dialog to determine the method you will use to define one or more objective functions for the optimization. The four options available in CHEMCAD are:
Each optimization objective is expressed as a function of process variables, which can be limited by constraints. These constraints might be physical, based on engineering knowledge about process or equipment limitations, or design constraints such as requirements from regulatory codes. Constraints are not required, but they can help streamline optimization solutions; this is because the optimization solver compares calculated values to the constraints and rejects any solution that falls outside of these limits.
In the following example, a mixture of air and sulfur dioxide is further oxidized to produce sulfur trioxide. The high-temperature gas stream (stream 1) is cooled using water in heat exchanger 1, which generates steam. The cooled air and sulfur dioxide stream (stream 2) is then pre-heated using the reactor effluent in heat exchanger 2, before being fed into the reactor. In this heat-integrated process, the optimization will aim to strike a balance between maximizing steam production while minimizing total heat exchanger area in an effort to reduce costs and footprint.
To set up this multi-objective optimization, open the Process Optimization dialog and click Define Objective Function. Then select Code in CHEMCAD Editor. Near the bottom of the Objective Function Code Editor dialog, you can see that the Complex method is used to minimize the first objective function (Obj1), the sum of the calculated areas for heat exchangers 1 and 2, and to maximize the second objective function (Obj2), the amount of steam generated.
After running a multi-objective optimization, you can utilize the Multi-objective Chart and Multi-objective Report commands in the Optimization group to analyze the results of the optimization. A Pareto front chart or report shows the relationships between the optima for your specified objective functions.
Optimization solvers can sometimes settle into local optima, which appear to offer a solution within a narrow range but are not the global solution to the optimization. CHEMCAD avoids this mathematical trap by automatically restarting the solver from multiple randomized starting points and parallelizing the process to efficiently search for the global optimum. This approach gives engineers greater confidence that the results in CHEMCAD reflect the best possible solution within the entire design envelope.
With energy costs rising, emissions regulations tightening, and efficiency targets becoming more ambitious, optimization is no longer a nice-to-have. It is a necessary process tool for staying competitive and compliant. CHEMCAD equips engineers with the ability to reduce costs, improve energy performance, and make informed, data-driven decisions.
Because optimization is integrated directly into the CHEMCAD simulation environment, engineers can quickly iterate, visualize results, and communicate findings with clarity. Whether the goal is cost savings, sustainability, or process reliability, solving complex optimization challenges can help your team move from assumptions to actionable improvements.