Process Simulation Techniques Research Articles

Biogas reforming as a sustainable solution for hydrogen production: Comparative environmental metrics with steam-methane reforming and water electrolysis in the Portuguese context

This study delves into the dynamics of hydrogen production, with a specific focus on biogas reforming (BGSMR) for hydrogen generation. It compares the environmental impact of this solution with hydrogen production from natural gas-steam reforming (NGSMR) and commercial electrolysis in the Portuguese context. Various metrics, including carbon footprint, water depletion, energy utilization, and waste valorization are employed for a comprehensive comparison. The assessment explores the impact of operational parameters and different off-gas combustion scenarios, incorporating water recycling practices. Due to challenges in obtaining detailed data on the actual reforming process, the study relies on process simulation techniques, primarily using DWSIM. Commercially available data for water electrolysers were used for comparison. In the context of decarbonizing power systems, hydrogen from water electrolysis emerges as a competitive option only in a scenario where the power system is 100% reliant on renewable sources, particularly with respect to the carbon footprint metric. Biogas systems, characterized by near-zero carbon emissions, stand out as a favourable option from the near future to the long run. This research contributes valuable insights into the dynamics of hydrogen production, shedding light on environmentally viable alternatives across a range of power system scenarios.

Solar-Assisted Carbon Capture Process Integrated with a Natural Gas Combined Cycle (NGCC) Power Plant—A Simulation-Based Study

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region.

Comparison of simulation model and plant data of a crushing-grinding circuit

Increasing complexity of ore deposits requires operations to be continuously improving their cost competitiveness. Mathematical modelling and process simulation techniques are useful tools to understand the process, define bottlenecks and increase process efficiencies. This paper presents a crushing and grinding circuit of a chromite ore gravity separation plant’ simulation model results and actual plant operating data. Throughout the study, jaw crusher, ball and rod mill summary equipment characteristics, breakage and selection functions employed, comparison of particle size distribution curves of site process streams to model results are presented. Simulation work is completed by using METSIM® software. The paper also showcases ease of flow diagram and process data presentation by the utilisation of the adequate simulation approach.

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Recent advances in technology and refinement of available computational resources paved the way for the extensive use of computers to model and simulate complex real-world problems difficult to solve analytically. The appeal of simulations lies in the ability to predict the significance of a change to the system under study. The simulated results can be of great benefit in predicting various behaviors, such as the wind pattern in a particular region, the ability of a material to withstand a dynamic load, or even the behavior of a workpiece under a particular type of machining. This paper deals with the mathematical modeling and simulation techniques used in abrasive-based machining processes such as abrasive flow machining (AFM), magnetic-based finishing processes, i.e., magnetic abrasive finishing (MAF) process, magnetorheological finishing (MRF) process, and ball-end type magnetorheological finishing process (BEMRF). The paper also aims to highlight the advances and obstacles associated with these techniques and their applications in flow machining. This study contributes the better understanding by examining the available modeling and simulation techniques such as Molecular Dynamic Simulation (MDS), Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Discrete Element Method (DEM), Multivariable Regression Analysis (MVRA), Artificial Neural Network (ANN), Response Surface Analysis (RSA), Stochastic Modeling and Simulation by Data Dependent System (DDS). Among these methods, CFD and FEM can be performed with the available commercial software, while DEM and MDS performed using the computer programming-based platform, i.e., “LAMMPS Molecular Dynamics Simulator,” or C, C++, or Python programming, and these methods seem more promising techniques for modeling and simulation of loose abrasive-based machining processes. The other four methods (MVRA, ANN, RSA, and DDS) are experimental and based on statistical approaches that can be used for mathematical modeling of loose abrasive-based machining processes. Additionally, it suggests areas for further investigation and offers a priceless bibliography of earlier studies on the modeling and simulation techniques for abrasive-based machining processes. Researchers studying mathematical modeling of various micro- and nanofinishing techniques for different applications may find this review article to be of great help.

Process Simulation Integrated Life Cycle Net Energy Analysis and GHG Assessment of Fuel-Grade Bioethanol Production from Unutilized Rice Straw

For the life cycle scenario of bioethanol production from unutilized rice straw, the life cycle stage of paddy rice cultivation can be excluded with a zero-inventory allocation rule, i.e., rice straw with no applied valorization in current practice. This study evaluates the life cycle net energy analysis and greenhouse gas (GHG) assessment for a scaled-up bioethanol production plant using unutilized rice straw as the feedstock. The process simulation technique is integrated to model a scaled-up production plant to produce bioethanol at 99.7 vol% purity from unutilized rice straw, and the simulation results are retrieved to calculate inventory data for the life cycle assessment (LCA). The simulated mass flow and energy flow results are comparable with that of real plants, reported in the published literature, which validates the process simulations in this study. Including energy generation using waste flows in the process (i.e., wastewater and solid residues), the life cycle net energy analysis results show a net energy gain of 7804.0 MJ/m3 of bioethanol with a net renewable energy gain of 38230.9 MJ/m3 of bioethanol that corresponds to a net energy ratio of 1.20 and renewability factor of 5.49. The life cycle GHG assessment exhibits a net global warming potential of 584.8 kg CO2 eq/m3 of bioethanol. The effect of system boundary expansion up to the end-of-life stage as gasohol (E10), the sensitivity of the key process parameters, and the economic benefit via valorization of unutilized rice straw are further analyzed and discussed.

Environmental sustenance via melon seed peel conversion to fermentable sugars using soft computing models

There is a shift in focus on agro-wastes constituting menace through their disposal in the environment by converting them to value-added products within the sustainability framework. This study aimed at predicting melon seed peel (MSP) hydrolysis parameters using soft computing models such as Artificial Neural Network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS). The capability of the models was evaluated using the correlation coefficient (R 2 ), mean square error (MSE), mean absolute deviation (MAD) and average absolute relative error (AARE). The experiments were conducted at an acid concentration (2.5–3.5 M), time (1–3 h), and temperature (80–100 °C) with output as sugar yield. The characterization results from the proximate analysis, FTIR, SEM, EDX, XRF and XRD indicated that MSP contains a significant amount of cellulose for fermentable sugar production. The sugar yield estimation performance indicators are: ANN (R 2 = 0.999, MSE = 1.2698E-12, MAD = 1.2%, AARE = 0.00231) and ANFIS (R 2 = 0.9782, MSE = 0.0334, MAD = 2.7%, AARE = 0.0054), these suggested a good agreement between the experimental and predicted values. The results showed that the ANN and ANFIS models effectively predict accuracy. It was concluded that soft computing models could be considered a viable chemical process modelling and simulation techniques.

Exact Simulation of Max-Infinitely Divisible Processes

Max-infinitely divisible (max-id) processes play a central role in extreme-value theory and include the subclass of all max-stable processes. They allow for a constructive representation based on the pointwise maximum of random functions drawn from a Poisson point process defined on a suitable function space. Simulating from a max-id process is often difficult due to its complex stochastic structure, while calculating its joint density in high dimensions is often numerically infeasible. Therefore, exact and efficient simulation techniques for max-id processes are useful tools for studying the characteristics of the process and for drawing statistical inferences. Inspired by the simulation algorithms for max-stable processes, theory and algorithms to generalize simulation approaches tailored for certain flexible (existing or new) classes of max-id processes are presented. Efficient simulation for a large class of models can be achieved by implementing an adaptive rejection sampling scheme to sidestep a numerical integration step in the algorithm. The results of a simulation study highlight that our simulation algorithm works as expected and is highly accurate and efficient, such that it clearly outperforms customary approximate sampling schemes. As a by-product, new max-id models, which can be represented as pointwise maxima of general location-scale mixtures and possess flexible tail dependence structures capturing a wide range of asymptotic dependence scenarios, are also developed.

Integrating Technoeconomic, Environmental, and Safety Criteria in Solvent Screening for Extraction Processes: The Case of Algae Lipid Extraction

Volatile organic solvents derived from fossil resources are typically used in extraction processes, but this usually involves high energy consumption for solvent recovery and negative environmental impacts due to solvents’ hazardous, volatile, and flammable nature. This study presents a systematic approach to solvent screening, using molecular and process simulation techniques, data analysis, and classification methods applying technoeconomic, environmental, and safety criteria. This methodology is demonstrated for lipid extraction from wet algae biomass in biofuel production. First, relevant thermodynamic equilibrium data are predicted with the Conductor-like screening model for real solvents (COSMO-RS) method. The resulting solvents are clustered according to their partition coefficient and selectivity toward the target solute and then screened further, considering their physicochemical properties and health, safety, and environmental (HSE) performance. Finally, the lipid extraction process is simulated in Aspen Plus using all screened solvents to obtain technical, economic, and environmental performance data. Out of 88 initial candidates, cyclohexane, limonene, and ethyl tert-butyl ether are identified as potential alternatives to the benchmark solvent, hexane. While these solvents tend to be more expensive and their recovery is more energy-intensive (higher boiling points) compared to hexane, they have a higher selectivity toward lipids, thus reducing the solvent intensity of the process, and are less volatile and nonhazardous according to the HSE classification. This methodology can be applied to other extraction process applications or implemented at early stages in the process design to evaluate technoeconomic, environmental, and safety trade-offs when considering and selecting more sustainable alternatives to fossil-derived solvents.

Modeling earthmoving operations in real time using hybrid fuzzy simulation

Predicting and optimizing performance in earthmoving operations is critical, because they are essential to many construction projects. The complexity of modeling earthmoving operations remains challenging, even with several modeling techniques available, including simulation. This paper advances the state-of-the-art of modeling earthmoving operations by introducing a hybrid fuzzy system dynamics–discrete event simulation framework with the capacity to: capture the dynamism of performance in earthmoving operations; capture subjective uncertainty of several factors affecting them; model their sequential nature and resource constraints; and determine actual travel time, in real time, using online navigation systems. Findings from this research confirm the proposed framework (i) extends the application of simulation techniques for modeling construction processes involving dynamic input variables and subjective uncertainty, through its ability to capture the non-probabilistic uncertainty of construction systems, and (ii) when combined with the use of online navigation systems to assess trucks’ travel time, improves the accuracy of earthmoving operation models.

Fast mask model for extreme ultraviolet lithography with a slanted absorber sidewall.

A fast mask model for extreme ultraviolet (EUV) lithography is vital to process simulation and resolution enhancement techniques. As the target pattern sizes have decreased, the impact of the absorber sidewall angle (SWA) has become a serious problem. In order to model the EUV mask with a slanted absorber sidewall quickly and accurately, a fast mask model based on the absorber sublayer decomposition is proposed. Since the absorber sidewall is slanted but not perpendicular to the multilayer surface, the absorber is decomposed into several thin pattern layers. For each thin layer, the diffraction is calculated by the edge point pulses model. The light propagation between two layers is calculated by spectrum superposition in the frequency domain with Hopkins frequency shift. The fast EUV mask model with slanted absorber sidewall is established by combining the accurate absorber model and the equivalent layer multilayer model. Simulations and comparisons validate the effectiveness of the proposed model. For the 22 nm vertical line-space pattern, the calculation errors of critical dimension via the proposed model are lower than 0.05 nm for different SWA values.

Thermoplastic Pultrusion: A Review.

Pultrusion is one of the most efficient methods of producing polymer composite structures with a constant cross-section. Pultruded profiles are widely used in bridge construction, transportation industry, energy sector, and civil and architectural engineering. However, in spite of the many advantages thermoplastic composites have over the thermoset ones, the thermoplastic pultrusion market demonstrates significantly lower production volumes as compared to those of the thermoset one. Examining the thermoplastic pultrusion processes, raw materials, mechanical properties of thermoplastic composites, process simulation techniques, patents, and applications of thermoplastic pultrusion, this overview aims to analyze the existing gap between thermoset and thermoplastic pultrusions in order to promote the development of the latter one. Therefore, observing thermoplastic pultrusion from a new perspective, we intend to identify current shortcomings and issues, and to propose future research and application directions.

Análise e simulação da gestão da produção de empresa: uma abordagem de dinâmica de sistemas

No âmbito da gestão da produção, devido à sua complexidade e dinamicidade, os gestores e demais tomadores de decisão devem planejar a produção e analisar o processo produtivo de forma efetiva e minuciosa. A fim de auxiliar no processo de tomada de decisão, existem diversas técnicas de modelagem e simulação de processos. Neste trabalho, o objetivo foi modelar e simular a gestão da produção em uma empresa que fabrica produtos ligados à comunicação visual com foco em produção para estoque, por meio da técnica de dinâmica de sistemas. Inicialmente, o modelo foi validado utilizando-se dados históricos e, posteriormente, simularam-se o cenário base e mais três cenários a fim de verificar o comportamento do sistema diante da redução do estoque desejado e do aumento da previsão de vendas. Concluiu-se que a análise e simulação da gestão da produção da empresa por meio da técnica de dinâmica de sistemas é viável e relevante para auxiliar os gestores na definição dos planos de vendas, estoque e produção e na identificação do impacto dos gargalos produtivos no sistema. Concluiu-se, ainda, que a atual capacidade produtiva da empresa analisada atende de forma satisfatória sua atual demanda.

Pattern Matching and Active Simulation Method for Process Fault Diagnosis

Fault detection and diagnosis is a crucial approach to ensure safe and efficient operation of chemical processes. This paper reports a new fault diagnosis method that exploits dynamic process simulation and pattern matching techniques. The proposed method consists of a simulated fault database which, through pattern matching, helps narrow down the fault candidates in an efficient way. An optimisation based fault reconstruction method is then developed to determine the fault pattern from the candidates, and the corresponding magnitude and time of occurrence of the fault. A major advantage of this approach is capable of diagnosing both single and multiple faults. We illustrate the effectiveness of the proposed method through case studies of the Tennessee Eastman benchmark process.

Thermal Analysis for Simulation of Metal Additive Manufacturing Process Considering Temperature- and History-Dependent Material Properties

Additive manufacturing (AM) technology is increasingly being used in the aerospace industry due to its advantages for aerospace components such as reduction of weight. A deep understanding of the behavior and properties of additively manufactured materials or parts is required to effectively carry out the certification process which is inevitable for aerospace components. However, since AM has so many parameters that affect the performance of products, the help of high-fidelity process simulation techniques is essential to fully analyze and understand their effects. In this research, we propose a new method to effectively implement the thermal analysis for process simulations of laser powder-bed fusion technique, a representative AM technique for metal materials, using existing commercial finite element analysis software. Thermal analysis for simulations of AM process is performed and the melt pool size is compared with test results to verify the accuracy of the simulation. In AM process simulations, material properties may vary significantly with temperature, and they are also dependent on the temperature history of the material because whether the current state is a powder or solid state is determined by the maximum temperature value in the past temperature history. Therefore, in this paper, user-defined subroutines and field variables are implemented so that the temperature history of each integration point for the finite element analysis can be properly tracked and appropriate material properties can be assigned accordingly. Using the proposed methods, thermal analysis for AM process simulations can be performed successfully with good accuracy compared with the existing test results.

Scenario Analyses for By‐Products Reuse in Integrated Steelmaking Plants by Combining Process Modeling, Simulation, and Optimization Techniques

Steel industry consumes resources and produces important amounts of by‐products. Process modeling, simulation, and optimization can address field tests for maximizing by‐products reuse. The improvements of a long‐lasting research activity on slag reuse by deepening aspects, only marginally considered in the past, are presented herein. An Aspen Plus‐based model is developed to assess different basic oxygen furnace slag pretreatment configurations or involved techniques. A previously developed superstructure is upgraded, considering more slag pretreatments and qualities. A simplified Aspen Plus‐based model of mixing of the different pretreated by‐products is applied to evaluate the chemical composition of pellets in each scenario, starting from the computed mixtures. The developed tools are exploited to get a holistic view of steelmaking by‐products reuse and make multiobjective optimization studies which provide different results. For instance, the mixture to be pelletized completely changes if final product quality is (or not) considered in the optimization. Regarding the optimization combining all the considered objective functions, the best slag treatment includes a wet high‐intensity magnetic separation stage, and only some slag qualities are considered in the pellet mixture. A preliminary economic analysis confirms that an optimal combination of internal and external recoveries of by‐products can produce noteworthy revenues.

Process simulation based life cycle assessment for bioethanol production from cassava, cane molasses, and rice straw

Abstract The process simulation technique is integrated into life cycle assessment (LCA) in this study to reduce biased parameters in process data collection and perform a fair comparison among different processes. Bioethanol production processes from cassava, cane molasses, and rice straw are studied as an example, to show the usability of process simulation. The simulation results are compared with actual bioethanol plant data to validate the reliability of the technique. The study is a comprehensive LCA comparison, based on unbiased process data from simulations, and minimum variations in other settings, i.e., process fuel type, waste recovery options, etc. The results show that cleaner process designs, using simulations with renewable process energy and sustainable waste recovery, improve the energy efficiency, renewability, and environmentally-benign aspects of the three bioethanol feedstocks. Bioethanol from cassava shows the best values of net energy ratio (1.34), renewability (5.16), and reduced greenhouse gas emissions (410 kg CO2 eq/1000 L). The unbiased LCA results from net energy analysis, and environmental impact assessment, including emissions/hotspot identification and sensitivity analysis, are comprehensively discussed. The simulation technique in this study is also adaptable for future LCA of new/modified processes.

Dynamical phase transition characteristics of FeC2 monolayer

The nonequilibrium dynamic phase transition properties of a two dimensional FeC2 monolayer in the presence of a time dependent sinusoidally oscillating magnetic field have been studied by using both mean field approximation with Glauber type of stochastic process and Monte Carlo simulation technique. The variation of the Curie temperature of the system with the amplitude and period of the external magnetic field have been investigated and temperature dependence of the magnetization of the system is presented for selected external field parameters. Moreover, the dynamic hysteresis curves of the system have been obtained for a wide range of temperature. Numerical results of the present study indicate the controllability of the dynamic magnetic properties of two dimensional FeC2 sheet by amplitude and period of the external magnetic field.

Simulation-Based Condition Assessment Model for Sewer Pipelines

Condition assessment models are considered as an evaluation tool that can be used by decision makers to accurately evaluate and assess the condition of pipes. Condition assessment of pipes helps in reaching better decisions in terms of repair or replacement before their failures. This paper presents a condition assessment model developed for gravity and pressurized pipelines in sewer networks using integrated fuzzy analytical network process (FANP) and Monte Carlo simulation techniques. Factors affecting gravity and pressurized pipelines in sewage networks were studied and included in the developed model. A questionnaire was distributed to experts in the field, to determine the weights of 17 factors for gravity pipelines, in addition to the operating pressure for pressurized pipelines. The developed model uses a weighted scoring system to determine a numerical value indicating the condition of pipelines. FANP was used to determine the weights of the factors affecting pipeline assessment, while Monte Carlo simulation was used to determine the final scores by probability distributions fitting. Actual data for an existing sewage network in the state of Qatar was used to validate the developed model. The developed model was able to satisfactorily assess the conditions of deteriorating sewer pipelines with an average validity of approximately 85%. The developed model is expected to be a useful tool for decision makers to properly plan for their inspections and provide effective rehabilitation of sewer networks.

Thematic issue: Advanced modeling and innovative processes

The thematic issue on BAdvanced Modeling and Innovative Processes^ is a result of a kind invitation from the Editorial Board of the International Journal of Material Forming. Therefore, the Invited Editors would like to acknowledge the Editorial Board, in the person of the Editor-in-Chief Prof. Francisco Chinesta, the ESAFORMAssociation, in the person of its President Prof. Dorel Banabic and, last but not least, Springer Editions for this great opportunity. The idea of this thematic issue is to give a summary (but comprehensive) overview of the most recent and diversified topics in the field of advanced modeling techniques, when directly applied to innovative processes. One of the main challenges of Engineering, in general aspects, is to provide a link between fundamental mathematical/physical concepts and industrial applications, leading to economic development as well as scientific one. This thematic issue is devoted to this landmark. The contributions chosen to be included in the present thematic issue try to provide, as mentioned, a wide variety of examples where this link can be achieved. For instance, polymer crystallization phenomena are dealt with and simulated by means of numerical techniques including multi-scale effects. In other publication, also dealing with constitutive modeling, nonquadratic anisotropic criteria are considered for the correct simulation of metallic alloys. In order to achieve a higher level of efficiency in the numerical algorithms to be used in industrial applications, procedures for obtaining equivalent parameters from different yield criteria and hardening laws are studied and analyzed in another contribution. Directly related to industrial applications and constantly seeking for efficient, real-time computations, another contribution proposes innovative ways to choose optimum tool paths for incremental forming processes, based on sequencing and multi-step mesh morphing procedures. From the point of view of reliability and robustness of numerical simulation results, the thematic issue presents a paper dealing with an inevitable nonlinearity in forming processes: contact modeling, and its influence in results as coming from early stage decisions on the way to properly describe tools’ surfaces. Also related to metal forming tools, another contribution deals with and presents a set of experimental setups able to properly investigate the wear of coatings, with results that can provide valuable insights into process simulation techniques, afterwards. Another analysis of coatings’ wear, but this time applied to turning operations, is given in another contribution, widening the range of applications included in this thematic issue. Modeling and numerical analysis should also rely, when possible, on inverse techniques and optimization and a contribution is included in this issue dealing with an integrated approach for the SHPB test and ranging from numerical to experimental analysis. Another relevant field of study in the processes’ modeling is the prediction of crack initiation and, subsequently, the monitoring of its growth. This topic is seen in a contribution dealing with FSW joining/processing techniques and the analysis of process parameters in the appearance of fracture zones and crack propagation. Finally, and also with the goal to widen the range of * Robertt Valente robertt@ua.pt

Production of phenolic and glucosinolate extracts from rocket salad by supercritical fluid extraction: Process design and cost benefits analysis

A profitability analysis of a large scale supercritical fluid extraction (SFE) process to obtain natural extracts from rocket salad is presented in this work. An industrial-scale SFE plant is designed using process simulation techniques based on experimental data. Afterward, the effect of operative parameters on the process costs is evaluated. At the optimum conditions, the economic and financial analysis is performed, and the influence of the price of rocket salad on the price of the natural extract is assessed. Results indicate that the raw materials account for 67% of the total production costs. The proposed industrial application could be economically viable with a pay-back period of 10years if the price of the rocket salad extract is larger than 21.9US$kg−1, considering the current wholesale price in the market, or 17.0US$kg−1 if using non-saleable rocket salad. Both scenarios make the process economically sustainable and competitive with the current price of natural extracts obtained by extraction with organic solvents.