Optimization Of Vacuum Pressure Swing Adsorption Process For Biogas Upgrading

As a kind of renewable and clean energy rich in methane, biogas has the potentialto replace the fossil fuels so as to relieve the global energy crisis. The original biogashas a low calorific value and its utilization efficiency is limited, because it containsplenty of CO2. Hence, removing CO2and improving the purity of CH4are the keyproblems to achieve the high utilization efficiency of the biogas. The three bedvacuum pressure swing adsorption process (VPSA) was applied in the biogas systemto separate CO2away from CH4, and the adsorbent used in the work was silicalite. Forthe sake of improving separation efficiency and economic benefit, the advancedynamic optimization method was adopted to optimize this separation process.gPROMS is set as the platform of dynamic simulation and optimization process.Based on it, a general model library, which includes all the unit operations of PSAprocess, is constructed. The library can provide the comprehensive description for anyPSA process. A strict mathematical model of the bed is built with gPROMS, and itdescribes not only the mass transfer, but also the heat transfer and driving within thebed in detail. Auto-switching boundary conditions of imports and exports areestablished to ensure the rationality of the bed model. The other auxiliary equipmentmodels in PSA process are also computed and analyzed, involving the buffer tank, thevalve, the compressor, the vacuum pump and so on and so forth. It is quiteconstructive to provide a real description for the PSA process. Furthermore, thethrottling effect between import and export of the ancillary equipment(valve, vacuumpump, compressor) is also considered in the work, which realizes the accuratedescription of heating effect of the pressure swing adsorption process.The partial differential equations (PDEs) versus time and space of themathematical model of PSA process are discrete in space by the second-ordercentered finite difference method. Then the PDEs are converted to ordinarydifferential equations (ODEs), and the ODEs can be solved by the fourth-orderRunge-Kutta integration method. The methods used to define the cyclic steady state(CSS) during PSA process are studied in this work, and the successive substitutionmethod is adopted to define the CSS, because it has good stability and convergenceduring the calculation process. The sensitivity analysis of design parameters is studiedin this paper with the reduced sequential quadratic programming (r-SQP) served asthe optimization method. Based on the above research, a strict and general optimization framework is constructed to optimize PSAprocess.An optimization for biogas upgrading process by three-bed and twelve-stepVPSA was performed by the above optimization framework in this work. Theoptimization goal is to maximize the recovery rate of methane (R) and the unitproductivity of adsorbent (uP), while to minimize the unit energy consumption (uW)under the condition of that CH4purity is not less than98%. The raw gas composed of60%CH4and40%CO2was used at313.15K with the pressure of101.325kPa.Initially, the purity of methane in the product is98.76%, the R70.93%, the uP0.1043Nm3CH4/kg/h, and the uW0.4423kWh/(kgCH4).After the38th optimization iteration,the optimal goal is obtained, and the solving accuracy and constraint conditions aremet as well. At that point, the product purity of methane is98.00%, the R93.23%, theuP0.1331Nm3CH4/kg/h, and the uW0.3697kWh/(kgCH4). The optimal results showthat it is extremely necessary to adopt this optimization framework to optimize thebiogas upgrading process. The R is increased by22.3%, the uP is increased by27.61%and the uW is reduced by16.41%. And therefore, the optimization of thisprocess has remarkable economic benefit.