Dynamic optimization of methanol synthesis section in the dual type configuration to increase methanol production

Abstract

The main object of this research is dynamic modeling and optimization of the methanol synthesis section in the dual type configuration considering catalyst deactivation to improve methanol production capacity. In the methanol unit, deactivation of CuO/ZnO/Al 2 O 3 catalyst by sintering and low equilibrium conversion of reactions limit the production capacity, and changing operating temperature is a practical solution to overcome the production decay. In the first step, the considered process is modeled based on the mass and energy balance equations at dynamic condition. To prove the accuracy of developed model, the simulation results are compared with the plant data at the same operating conditions. In the second step, a dynamic optimization problem is formulated, and the optimal trajectories of manipulated variables are determined considering methanol production rate as the objective function. Finally, the performance of optimized process is compared with the conventional system at the same design conditions. The results show that operating at the optimal conditions increases methanol production capacity about 6.45%. Nomenclature aActivity of catalyst (-) A C Cross section area of each tube (m 2 ) CtTotal concentration (mol m À3 ) C p Specific heat of the gas at constant pressure (J mol À1 K À1 ) DDiameter (m) E d Activation energy used in the deactivation model (J mol À1 ) F t Total molar flow rate (mol s À1 ) f i Partial fugacity of component i (bar) K i Adsorption equilibrium constant for component i (bar À1 ) K p Equilibrium constant based on partial pressure for component i (-) k 1 Reaction rate constant for the first rate equation (mol kg À1 s À1 bar À1/2 ) k 2 Reaction rate constant for the second rate equation (mol kg À1 s À1 bar À1/2 ) k 3 Reaction rate constant for the third rate equation (mol kg À1 s À1 bar À1/2 ) K d Deactivation constant (h À1 ) P Total pressure (bar) r 1Rate of reaction for hydrogenation of CO (mol kg À1 s À1 ) r 2 Rate of reaction for hydrogenation of CO 2 (mol kg À1 s À1 ) r 3Reversed water-gas shift reaction (mol kg À1 s À1

Century of Technology Trends in Methanol Synthesis: Any Need for Kinetics Refitting?

“…However, on an industrial scale, it is common practice to feed methanol reactors with syngas close to the stoichiometric condition (optimal SN is 2.05 as investigated by Løvik and co-workers) and excess of H 2 (2 < H 2 /CO < 3) with a variable amount of CO 2 (i.e., variable COR) according to the installed syngas generation technology. This means that the most common case in an industrial plant is represented by bio-syngas and petro-syngas feedstocks as reported in the literature case studies ,, and real methanol industrial plants’ data. , However, CO 2 /H 2 mixtures as feedstocks for methanol synthesis are under investigation, and they are becoming more and more appealing for their sustainability and environmental reasons ,− although carbon capture utilization and sequestration (CCUS) still represents a relevant cost factor in industrial plants as recently reported by the IEA…”

The mathematical catalyst deactivation models: a mini review

“…55 Accordingly, the deactivation model derived by Honken, 56 shown in eqn (12), was found to be suitable for representing the catalyst deactivation in many industrial scale applications. [57][58][59][60][61][62][63] Rahimpour et al 64 applied a seventh-order catalyst deactivation model to study the dynamic catalyst deactivation from carbonic coke formation during naphtha reforming reactions at 502-504 °C and pressure 34-37 bar, in which carried out in a radial ow spherical reactor as shown in eqn (13).…”

Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization

“…Masoudi et al succeeded in increasing the methanol production capacity by 6.45% over a conventional dual-type reactor through the dynamic modeling of a dual-type methanol synthesis section. They considered catalyst deactivation and optimization of the feed temperature, cooling water temperature, and other factors to manage the heat . Pérez-Fortes et al developed a technology for converting CO 2 to DME through the three-stage reformation of methane, a low-cost feedstock, with a DME synthesis unit …”

“…To optimize the design variables efficiently, we use machine learning. Askari et al and Masoudi et al applied a genetic algorithm to a methanol synthesis section and improved the methanol production capacity. , Omata et al combined a genetic algorithm with a neural network to optimize the temperature profile of a temperature gradient reactor, thus improving the conversion of CO under low-pressure conditions . Previous studies have optimized the design variables for certain subprocesses, but the entire process has not been considered in the optimization.…”