Simulation of Wood Drying Process with a Sensitivity Analysis

Purpose: To study a case of modeling an industrial distillation system, using the Aspen Plus simulator as the mathematical model and subsequently generating surrogate models using Machine Learning techniques in Matlab. Theoretical Framework: Metamodels are reduced models obtained from data generated by rigorous models, replacing them entirely or partially when the computational codes originating from them require excessively large computational effort to be used feasibly. Method/Design/Approach: The simulation of the process was performed in Aspen Plus. Subsequently, the most important variables were selected, and an experimental design was created using Latin Hypercube Sampling in Matlab, generating points to be used in sensitivity analysis in Aspen Plus. The next step was the construction of metamodels using the Statistics and Machine Learning toolbox in Matlab, employing linear regression and Gaussian process regression models. Finally, a statistical analysis was conducted. Results and Conclusion: The distillation system simulation converged, and the obtained metamodels had good regression indicators, especially the Gaussian process regression models, making them the most suitable for representing the rigorous Aspen Plus model. Research Implications: Understanding the multicomponent distillation process integrated with computational tools and data regression models, leading to a reduction in computational effort. Originality/Value: Development of a tool that enables the simulation and evaluation of a distillation process without the need to acquire software with rigorous equation databases.

Over the last few decades, research on the abatement of carbon dioxide (CO2) gas has gained momentum, due to its increasing atmospheric levels. This study investigated high‐temperature steam‐only gasification of woody biomass for the production of high‐purity hydrogen integrated with CO2 capture in a moving‐bed gasifier. Extensive process modelling and simulation were performed using the superior solid handling features of the Aspen Plus process simulator software. After validating the model with experimental data from a demonstration plant available in the open literature, a reversible carbonation‐calcination reaction of calcium oxide (CaO) with CO2 was added to the system. Sensitivity analyses were conducted to verify the predictive accuracy of the model. The effects of steam‐to‐carbon (S/C) ratio on the resulting gas composition were thoroughly studied to delineate the complex process of gasification. Beyond the mitigation of CO2 emissions, the introduction of a CaO‐based sorbent in the process simulation significantly enhanced hydrogen production by simultaneously promoting the forward water‐gas shift reaction and reducing tars through increased tar‐cracking reactions. The results show that hydrogen of a higher purity was produced with the inclusion of dry‐sorption CO2 capture in the gasification process. Moreover, the addition of the sorbent increased the higher heating values (HHV) by 3 times and improved the cold gas efficiency by 34 %.

CFD simulation of the Sabatier process in a shell-and-tube reactor under local thermal non-equilibrium conditions: Parameter sensitivity and reaction mechanism analysis

ABSTRACTHydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.

Operational digital twins bring together digitalization technologies, including machine learning, IIoT, data analytics, process simulation and optimization. A digital twin model reconciled with real-time process data provides the foundation for a range of layered applications that are key to transforming the way that process plants and value chains are operated and managed. After outlining the basic numerical methods, this paper will describe three industrial applications of dynamically reconciled digital twin models in gas processing, refining and olefins production. The focus is on application robustness and high availability, delivering increased operating margins and plant flexibility. An operational digital twin model can provide a versatile tool for real-time process optimization, and what-if analysis, as well as operations planning and scheduling. To be effective, the digital twin model needs to be reconciled with real-time process data frequently. In a dynamic operating environment where feed composition, process conditions and product demand may change continuously, this challenging data reconciliation task is solved using a combination of numerical methods: Equation-oriented model structures and solvers provide the flexibility required to solve simulation, parameter estimation and optimization cases. Extended Kalman filter and moving horizon estimation enable reconciliation using real-time dynamic data without the need for steady-state operation. Process models are developed using both first principles and data-driven surrogate modelling methods. A dynamically reconciled digital twin model provides the basis for an equation-oriented multi-period optimizer that can be solved for facility-wide steady state optimization, short-range dynamic optimization and long-range planning and scheduling problems within a single unified modelling and optimization framework. The use of hybrid models that include both data-driven and first principles components can significantly reduce the deployment and maintenance effort, the resulting low-order models facilitate model reuse across the different optimization horizons and objectives and enable large-scale optimization problems to be solved across an entire process plant or value chain. Three industrial applications of these methods will be outlined. The first is a gas plant optimizer that provides real-time process optimization and what-if analysis. The reconciled gas plant model tracks the process under significant feed and ambient variations. The second application is a refinery-wide optimizer that encompasses multiple crude distillation, vacuum and reaction units. The third application is an olefins process scheduling application that optimizes feed distribution, cracking furnace operating conditions and decoke cycle over an extended future time horizon.

The impurity of benzothiophthene can be removed by extractive distillation in the crude naphthalene purification process. A process simulation has been studied on the crude naphthalene purification. In the case of ensuring each fraction's naphthalene concentration, a sensitivity analysis is made to determine the optimal conditions by varying extractive distillation parameters such as the feed stage, the reflux ratio and the distillate rate. Besides controlling of the sensitive plate for the recovery section is also discussed.

Shifts in migration and border control policies may increase the likelihood of trauma exposure related to child–parent separation and result in costs to the health system and society. In the present study, we estimated direct and indirect costs per child as well as overall cohort costs of border control policies on migrant children and adolescents who were separated from their parents, detained, and placed in the custody of the United States following the implementation of the 2018 Zero Tolerance Policy. Economic modeling techniques, including a Markov process and Monte Carlo simulation, based on data from the National Child Traumatic Stress Network's Core Data Set (N = 458 migrant youth) and published studies were used to estimate economic costs associated with three immigration policies: No Detention, Family Detention, and Zero Tolerance. Clinical evaluation data on mental health symptoms and disorders were used to estimate the initial health state and risks associated with additional trauma exposure for each scenario. The total direct and indirect costs per child were conservatively estimated at $33,008, $33,790, and $34,544 after 5 years for No Detention, Family Detention, and Zero Tolerance, respectively. From a health system perspective, annual estimated spending increases ranged from $1.5 million to $14.9 million for Family Detention and $2.8 million to $29.3 million for Zero Tolerance compared to baseline spending under the No Detention scenario. Border control policies that increase the likelihood of child and adolescent trauma exposure are not only morally troubling but may also create additional economic concerns in the form of direct health care costs and indirect societal costs.

The World Energy consumption has been increasing steadily since industrialization, this recent increase is also the major cause for the raise of CO2 concentration in the atmosphere. Fossil fuels play a central role in our energy consumption; actually the CCS technology and their operations in power systems must get a prominent role in reducing total CO2 emissions. An attempt to tackle the problem of solvent based Post Combustion Carbon Capture process optimization requires the availability of a rigorous process model along with a design methodology. During the modeling, much physical and chemical process should be considered in order to get more realistic results, this complexity process addressed as Reactive Separation. This report presents detailed descriptions of the process sections as well as technical documentation for the ASPEN Plus simulations including the design basis, models employed, key assumptions, design parameters, convergence algorithms, concentration and temperature profiles and calculated outputs. The main purpose is to minimize the amount of energy required in the desorption process through the optimum operating condition to the actual CO2 absorption experimental setup. The case of study is on MEA 30wt% in a coal Hired power plant. Electrolytic method is considered; the sensitive analysis was used for the Optimization.

The direct relationship graph (DRG) and sensitivity analysis were used to simplify the detailed mechanism of methane combustion in GRI-Mech 3.0, and finally a reduction mechanism containing 26 components and 64 elementary reactions was obtained. To verify the rationality and reliability of the reduction mechanism, the closed homogeneous reactor was used to verify that the deviation between the ignition delay time predicted by the reduction mechanism and the detailed mechanism was less than 5 %. The simulation results of the fully stirred reactor (PSR) showed that the error of the combustion temperature calculated by the reduction mechanism did not exceed 2 %, and the peak deviation of the NO mole fraction was controlled within 8%; in the laminar premixed flame simulation, the peak error of the combustion velocity predicted by the reduction mechanism was less than 3 %. Further verification by the Sandia Flame D flame experiment showed that the maximum deviation between the temperature distribution simulated by the reduction mechanism and the experimental data was 44.7 K, while the relative errors of the mass fraction distribution of components such as CH4, O2, H2O and CO2 and the experimental values were all less than 5%. Research has shown that this reduction mechanism significantly reduces the computational complexity while ensuring that the error of key parameters is less than 5 %. It is suitable for numerical simulation of complex combustion processes and provides efficient kinetic model support for engineering combustion optimization and pollutant control.

To determine the optimum process parameters and loading paths for T-branch tube compound bulging using rubber medium, a finite element model was established at the software platform of ANSYS/LS-DYNA. Tubular blank parameters were optimized while the affections of blank parameters and frictions in bulging process were analyzed, during which Taguchi method, sensitivity analysis and normalization method were adopted and an evaluation function is inducted. The Taguchi method was used to establish a design of simulation schemes, and numerical simulations were carried; a sensitivity analysis was carried out with analysis of variances to seek the significance of parameters; the evaluation function was inducted to synthetically evaluate the affections of the thickness variation and the branch height and normalization method are applied to ensure equality of two sets of data in the evaluation function. Then the execution method and loading path of counter force for compound bulging process were designed and an ideal T-branch tube was gained at last.

Medical isotope test reactor is the first reactor in the world. In order to implement the principle of minimizing radioactive waste, the radioactive waste liquid adopts advanced drying and salt formation technology. According to the current dry salt formation technology a number of problems arise due to the complexity of the feedstock waste stream. In this study, a thermodynamic electrolyte simulation model was developed and its predictive accuracy was verified for complex electrolyte systems. Then, sensitivity analysis and discussion of the operating parameters such as temperature, pH and salt adjustment of the dry salting technology are presented. The new approach of the thermodynamic electrolyte simulation model has been used to improve and optimise the safety aspects of the technology and the associated process configuration. It provides a certain general idea and follow-up theoretical guidance for drying salt formation technology.

The main objective of this study is to design and simulate three different continuous processes, namely, homogeneous and heterogeneous alkali-catalyzed and supercritical methanolysis processes to produce biodiesel at a production rate of 100,000 t/year from virgin vegetable oil. Tetrahydrofuran (THF) was used as a cosolvent at different concentrations of 25 wt.%, 30 wt.%, and 1.63 wt. % for the homogeneous and heterogeneous alkali-catalyzed and supercritical processes, respectively. An economic assessment and a sensitivity analysis were performed based on the results of the process design and simulation. Technical assessment of the proposed processes indicated that the homogeneous and heterogeneous alkali-catalyzed processes were the simplest, where the least amount of process equipment were used. whereas the supercritical methanolysis process was more complex, which used a large number of transesterification and separation units. The homogeneous alkali-catalyzed process that used THF demonstrated the lowest total capital investment, after-tax net profit, and payback period of M$2.32, M$10.54, and 0.19 years, respectively, and the highest after-tax rate of return of 513%. However, the heterogeneous alkali-catalyzed process that used THF demonstrated the lowest manufacturing cost of M$82.20 and the highest after-tax net profit of M$18.20. The supercritical methanolysis process that used THF demonstrated the highest manufacturing cost of M$90.07 and the after-tax net profit of M$12.40. The results from the sensitivity analyses indicated that the methanol recovery percentage, biodiesel purification tower vacuum pressure, and costs of feedstock oil, methanol, biodiesel, and glycerin by-products are the factors that most significantly affect the economic feasibility of biodiesel production.

Biogas is a renewable energy source and needs to be upgraded to biomethane for injection into the natural gas grid or use as fuel. To design ionic liquid (IL) solvents for biogas upgrading, a computer-aided ionic liquid design (CAILD) method and the corresponding process simulation are presented. The UNIFAC-IL model is employed to calculate the solubility of gases in ILs, while group contribution (GC) based models are used to predict the physicochemical properties of ILs. By using the performance index (PI) as the objective function and the structural feasibility and physicochemical properties as constraints, a mixed-integer nonlinear programming (MINLP) problem is formulated and solved by the generate-and-test method. Two IL solvents, [MMPY][Tf2N] (1,3-dimethylpyridinium bis(trifluoromethylsulfonyl)imide) and [MMPY][eFAP] (1,3-dimethylpyridinium tris(pentafluoroethyl) trifluorophosphate), are found to be the optimal IL solvents from 880 IL candidates. To perform the process simulation by using the designed ILs, the parameters for the equations of calculation of required properties are regressed. A sensitivity analysis is performed to find the optimal conditions for the process. Finally, the developed process is compared with the water-scrubbing process for biogas upgrading.

A conceptual design of coffee stem biomass gasification was developed using the Aspen Plus process simulator, which was analyzed based on energy criteria such as the lower heating value of the produced synthesis gas and cold gas efficiency. The results yielded a lower heating value of 5.07 kJ/MJ, with a cold gas efficiency of 77.45% and a yield of 2.75 kg gas/kg biomass, all operating under optimal conditions with studied parameters including an air-to-biomass ratio between 1.8 and 2.5, gasifying agent temperature of 260 °C, and biomass moisture content below 10%. A sensitivity analysis was conducted on the formation of the most important compounds for synthesis gas quality (H2, CO, CO2, H2O, CH4). This involved understanding the reactions occurring throughout the gasification reactor and evaluating process parameters such as feed moisture content, gasification section temperature, and the ratio between feed and gasifying agent flows to optimize the process with the aim of improving product quality and reducing residue formation. The simulation scheme was validated against experimental data, yielding results consistent with reality. This contributed valuable information towards process optimization for projects targeting the Colombian coffee sector, paving the way for future gasifier designs tailored to the quantity of raw material to be processed.

Techno-economic analysis for evaluating the potential feedstocks for producing hydro-processed renewable jet fuel in Taiwan