Methanol is an important compound widely used in the chemical industry. Today, it is largely produced using fossil-based resources. Synthesizing methanol from renewable energy poses the challenge of the varying availability of green hydrogen as a feed stream. Conventional processes are not designed to consider such fluctuations.

The objective of this paper is to identify the optimal design of a multi-stage reactor for green methanol synthesis with inlet fluctuations. The authors model the reactor’s design parameters and operating conditions and propose an optimization problem to maximize the methanol yield, subject to a minimum carbon conversion rate. They solve the problem in the nominal steady-state and in a robust steady-state version, which is feasible for different inlet fluctuation values. However, through numerical simulations, they show that these models are not feasible when transient behavior is considered.

They hence propose a dynamic programming model that allows the feed distribution and shell temperatures of the reactor to change over time, depending on the varying value of the inlet stream. Experiments show that this problem is feasible for fluctuating inlet conditions.

All three optimization problems are continuous and nonlinear and require a discretization of the infinite dimensional problem to be solved. The authors exploit the power of Artelys Knitro with the multi-start option to solve all three of them.