Fuel-cell efficiencies yield substantial reductions in the emissions of climate-change gases and promise an end to exclusive reliance on carbon fuels for energy. Fuel cells, CO2 reuse, process heat integration, and open gas turbine electricity cogeneration can be optimized simultaneously, using a nonlinear programming (NLP) algorithm. The simplified NLP model contains equations of structural and parametric optimization. This NLP model is used to optimize complex and energy-intensive continuous processes. This procedure does not guarantee a global cost optimum, but it does lead to good, perhaps near-optimum, designs. The plant, which produces methanol, has a surplus of hydrogen (H-2) and CO2 flow rates in purge gas. H-2 is separated from the purge gas by an existing pressure swing adsorption (PSA) column. Pure H-2 can be used as fuel in fuel cells. CO2 can be separated from the outlet stream (purge gas) by a membrane or absorption system (absorber and regenerator) or an adsorption system and reused as a reactant in a reactor system. Therefore, the product yield can be increased and CO2 emissions can be reduced, simultaneously. CO2 emissions can then be reduced at the source. The retrofitted process can be operated within existing parameters. Using a methanol process as a case study, the CO2 emission flow rate can be reduced by 4800 t/a. The additional electricity cogeneration in the gas turbine and in fuel cells and additional flow rates of the raw material could generate an additional profit of 2.54 MEUR/a.