This paper describes the application of a nonlinear programming (NLP) mathematical method within an existing process, using simultaneous process and energy system multi-parameter optimization. It presents an expansion of a retrofit design case study using Combined Heat and Power (CHP), with the following new topic as key themes: including degrees of conversion at the catalyst bed-level in the reactor model, using hydrogen separation from purge gas, and energy generation using hydrogen fuel cells. The aim of this article is to connect process optimization with renewable energy generation, using waste hydrogen from the purge gas of the methanol reactor as fuel in the fuel cells. The separation of hydrogen is a continuous process of cleaning waste H₂, without costly production and storage of the fresh one. Fuel cells and open gas turbine electricity cogeneration can be optimized simultaneously using the NLP algorithm. The NLP model contains equations for parametric optimization, including degrees of conversion at successive catalyst bed-levels. The NLP model is often used to optimize complex and energy intensive continuous processes. This procedure does not guarantee the global cost optimum, but it does lead to good designs, perhaps near-optimum ones. The optimization approach is illustrated using a complex low-pressure Lurgi methanol plant, giving an additional profit of 2.65 MEUR/a. The plant, which produces methanol, has a surplus of hydrogen (H₂) flow rate in its purge gas. H₂ should be separated from the purge gas by an existing pressure swing adsorption (PSA) column. Pure H₂ can be used as fuel in the hydrogen fuel cells.