Simulation Of Production Process Research Articles

Simulation and conceptual design of an aniline production process from catalytic hydrogenation of nitrobenzene in ChemCAD

Aniline is a very important compound in organic chemistry, mainly used to synthesize polyurethane and its precursors. In the present work, the simulation and conceptual design of an aniline production process through the catalytic hydrogenation route of nitrobenzene was carried out using ChemCADÒ simulator. The simulated aniline production process consisted of a fired heater, a tubular reactor, several heat exchangers, a phase separator, a decanter, as well as two distillation columns. By means of the simulation, the temperatures, pressures, mass flowrates and enthalpies of the main intermediate and final streams were known, as well as the main operating and design parameters of some equipment. About 1,743 kg/h of aniline with a purity of 99.78% are produced at the top stream of the second distillation column, while about 49,881 kg/h of hydrogen are obtained as a by-product in the phase separator. In this work a first-of-its-kind ChemCAD simulation model was obtained in this work, which can be utilized for further optimization studies and tasks involving the increment of aniline purity, yields and productivity.

Carbon dioxide utilization in propylene carbonate production process

Utilizing carbon dioxide in chemical processes is one of the suggested remedies for reducing its atmospheric concentration. In this paper, the simulation of the propylene carbonate production process using 1,2-propanediol and carbon dioxide has been performed. The impact of temperature was examined between 75 and 400°C. It has been found that as temperature rises, reaction conversion increases. Additionally, the impact of recycling ratio was examined in the range of 0.1–0.5, which demonstrates that a rise in the recycle ratio results in a fall in conversion. Furthermore; it was observed that the pressure initially increases the solubility of carbon dioxide in 1,2-propanediol and improves the conversion of the reaction, but when enough carbon dioxide is supplied in the reaction, increasing the pressure does not affect the corresponding reaction. The effect of all studied parameters depends on the residence time of the reactants in the reactor. Investigating the interaction of parameters and optimizing the process has shown several optimal point of the process such as temperature of 295 °C, a recycle ratio 0.1, feed ratio 0.8 and a residence time of 12.61 h, with the related conversion being 59.6%.

Digital foundry production: modeling systems, personnel training, digitization stages.

The article describes modern digital solutions for production automation, simulation of production processes in order to optimize technology, eliminate defects, improve manufacturability of products, reduce production costs, as well as the company's services in the implementation of calculation projects and training, including reverse engineering of products, opportunities for advanced training. As an example of the implemented calculation is presented the work on optimization of the design of mold used for casting copper ingots in order to improve the durability of equipment; simulation of cyclic thermal heating of mold taking into account stress-strain state and fatigue criterion was performed in the software ProCAST. An example of automatic optimization of the design of feeding system for cast iron casting “pump body” using ProCAST and pSeven packages is also presented. The optimization package performed the rebuilding of 3D model in CAD-system with the change of height and diameter of feeder, calculated the case of filling the mold and solidification of the casting taking into account the changed geometry in the calculation CAE package and evaluated the obtained values of porosity in the casting and yield to find the optimal size of feeder

Simulation and conceptual design of a dl-methionine production process by the chemical synthesis route

Methionine is one of the two sulfur-containing amino acids demanded in the diet of humans, other mammals and avian species, while most of the produced methionine is consumed as an animal feed additive, especially in livestock production and the poultry market. In this paper, the techno-economic assessment and conceptual design of an industrial scale, feed grade dl-methionine production process was carried out using SuperPro Designer simulator. The plant was designed to produce 109 tons of dl-methionine per year through the chemical synthesis route. The total capital investment and annual operating cost of the proposed plant were USD $ 8.282 million and USD $ 1.323 million, respectively, while the net present value, the internal rate of return and payback time of the project were USD $ 4.436 million, 20.33 % and 4.74 years, respectively. The findings indicated that the proposed production process is feasible and profitable for a dl-methionine selling price of USD $ 35/kg. The innovative simulation model obtained in this work could be used for further optimization studies, as well as to increase the productivity and profitability of the proposed plant.

Energy efficiency of hydrogen production via gasification from lignocellulosic residual biomass blend

Facing the challenges of environmental sustainability and energy security caused by anthropogenic carbon emissions, there is a need to adopt cleaner energy generation technologies, leveraging Colombia's existing national resources. In this context, hydrogen emerges as a promising source of renewable energy. Therefore, this project explores the use of a blend of residual lignocellulosic biomass as raw material for hydrogen production through gasification for energy purposes. Initially, a screening of residual lignocellulosic biomass in the study region was conducted, a blend was selected, and a simulation of the synthesis gas production process was carried out prospectively using Aspen Plus Dynamics® software. The results revealed that, by using the selected biomass blend, a synthesis gas with a hydrogen molar fraction of 38.7% and an ER of 0.19 was obtained. According to sensitivity analyses, the optimal parameters identified to achieve this hydrogen concentration were: gasification temperature of 707°C, oxygen flows of 484 kg/h, steam at 420 kg/h, and gasification pressure of 1 atm. These findings support the potential of the studied lignocellulosic biomass blend as an alternative for hydrogen production, while also offering an opportunity for the valorization of lignocellulosic residual biomass.

Advancing Drug Delivery in Colorectal Cancer: A Comprehensive Exploration of Nanotechnology, Experimental Models, and Production Process Simulation

Colorectal cancer (CRC) remains a major global health challenge as the third most common cancer worldwide. Survival relies heavily on early diagnosis and treatment before progression to metastasis. Employing nanotechnology for targeted drug delivery shows promise to enhance therapeutic efficacy and tolerability in CRC. Diverse nanoparticle platforms offer unique advantages as CRC drug carriers with tunable properties like improved pharmacokinetics, stability, sustained release, and tumor cell uptake through endocytosis or transcytosis. Key platforms highlighted include liposomes, polymeric micelles, solid lipid nanoparticles, nanodiscs, cubosomes, and polysaccharide particles. In vivo testing is warranted to translate multiparameter optimized carrier designs to clinical applications. Simulations help predict carrier localization, biodistribution, controlled release, and optimal formulations. Overall, nanomedicine promises more targeted therapeutic delivery to improve survival for CRC patients, especially those with advanced or metastatic disease. However, realizing this potential requires additional pharmacodynamic studies alongside mechanistic clarification and carrier optimization to progress designs to clinical evaluation. Advancing personalized, targeted nanotechnology-enabled treatment options remains imperative.

Integrating multiple industry 4.0 approaches and tools in an interoperable platform for manufacturing SMEs

Small and medium-sized enterprises (SMEs) often struggle to optimize their production processes and products due to limited resources and lack of expertise in technological solutions. To address this issue, this research work presents a multi-purpose platform that can be used by SMEs as easy-to use support tool to run what-if analyses to optimize their products and production processes design. The platform fulfills the requirements of interoperability, accessibility, and usability and is built upon a layered infrastructure, consisting of specific modules within its Decision-Making Layer, Data Exchange Layer, and User Management Layer. The platform integrates several software, including Plant Simulation, Autodesk Inventor Nastran, and Autodesk Inventor Nesting, to simulate production processes, to run finite element analysis, and to optimize raw material utilization. The interoperability and data exchange are ensured through the adoption of FIWARE, an open-source framework for IoT platforms, which enables the harmonized representation of data formats and semantics among different applications and software within the platform. The User Management Layer allows SMEs to easily interact with the platform, making it an easy-to-use tool even for those with limited IT competencies. A case study conducted by an SME in the furniture sector demonstrated the platform's effectiveness in achieving optimal design and production of a new bookcase, resulting in increased efficiency, reduced costs, and improved profitability. The modular structure of the platform enables potential future integration of additional modules, such as quality control, supply chain management, etc., further enhancing its capabilities and potential benefits for SMEs. The proposed multi-purpose platform represents a valuable tool for SMEs to optimize their production processes and products and to overcome the limitations due to their limited resources and expertise in technological solutions.

Simulation and multi-objective optimization of the dimethyl carbonate production process

Greenhouse gases such as CO2 are considered effective materials in global warming due to their high absorptivity. So, lowering atmospheric CO2 is one of the most practical strategies. Utilizing carbon dioxide in chemical processes is an applicable method. In this study, Aspen HYSYS 10 was used to investigate how carbon dioxide can be added to the process of dimethyl carbonate production, and the affective parameters of the process, including temperature, residence time, feed ratio, and recycle ratio, were evaluated. It was observed that the production of DMC grew as temperature rose. The simulation results also revealed that a maximum conversion of roughly 8% was attained in the MeOH/EC. Additionally, boosting the recycle ratio is detrimental, and the impact of temperature and MeOH/EC has been enhanced by increasing the residence time. The interactions of the above parameters have been studied by Design Expert 12. The optimum value of effective parameters for the production of dimethyl carbonate has been obtained as follows: temperature of 164.7° C, recycle ratio of 0.2, residence time of 139.45 min, and feed ratio of 5.9%, leading to the conversion of 70%.

Influence of cationic surfactants on the growth of gypsum crystals

Abstract The effect of cationic surfactants on the growth of gypsum was evaluated under conditions of a simulated production process of gypsum desulfurization. We used a laser particle size analyzer and a microscope to determine the particle size and morphology. The growth rate of gypsum was determined by the equation for the rate of chemical reaction of CaSO4·2H2O. The results showed that the growth rate at a supersaturation ratio of 2.4 increased by 50% with 30 mg/L of a dodecyldimethylbenzylammonium chloride. The gypsum morphology changed from needle-like in the absence of additives to tabular in the presence of cationic surfactants, indicating that relatively thicker and larger crystals were formed. Finally, the investigated cationic surfactants were beneficial to the normal operation of the desulfurization process.

METHOD FOR THE INTEGRATION OF COMPUTER AIDED MANUFACTURING DATA IN LIFE CYCLE ASSESSMENT

AbstractPrecise sustainability assessment becomes increasingly important in decision-making, marketing, and regulations. Therefore, reliable and comparable LCA becomes mandatory. Currently, primary data is rarely available due to vastly complex value chains. Secondary data from eco-databases provide a remedy to estimate the sustainability impacts of up- and downstream processes. While giving insights and estimations, this data is seldom fitting exactly to the own processes and lacks comparability. Therefore, this paper proposes a method to close the gap between unreliable secondary data and unavailable primary data. This gap is to be closed by the integration of simulated process data. CAM is a tool during the work preparation to assess the design's manufacturability, decrease set-up times and optimize the NC code. However, integrating DES into LCA is still subject to research and will be discussed in this paper. This paper answers the question of the necessary steps to integrate the simulated production process in an LCA to increase the quality and reliability of sustainability indicators. A method is presented, and the implementation of the steps with the help of a developed assistance system on an example is performed.

A Simulation and Thermodynamic Improvement of Methanol Production Process with Economic Analysis: Natural Gas Vapor Reforming and Utilization of Carbon Capture

A Simulation and Thermodynamic Improvement of Methanol Production Process with Economic Analysis: Natural Gas Vapor Reforming and Utilization of Carbon Capture

A Prototype of a Domain-Specific Modeling Language for Formal Specification of a Human Worker

Abstract The application of innovative technologies that foster smart production resources’ interconnectivity alongside the virtual space that facilities to support process simulation makes the technology factor become the center of Industry 4.0. The production process modeling and simulation can be used to facilitate flexibility and automation of a shop floor. In our previous research, we have created a Domain-Specific Modeling Language (DSML) named MultiProLan, aiming to create production process models suitable for the automatic generation of executable code that enables the automatic execution of production processes. As the next step, we have proposed research on a DSML language aimed at Industry 4.0 human worker modeling. Industry 4.0 still considers workers as a cost, while favoring technological aspects over the workers’ wellbeing. Industry 5.0 complements Industry 4.0, focusing on a human-centric approach that puts core human needs and interests at the top of production processes. Based on our research proposal, here we present a blueprint of the HResModLan DSML prototype aimed at the formal specification of a human worker within Industry 5.0. Presented abstract and concrete syntaxes of the language are tested on a case study of a furniture factory to demonstrate whether they are a good base for the further development of the HResModLan language.

IIoT-Supported Manufacturing-Material-Flow Tracking in a DES-Based Digital-Twin Environment

Manufacturing processes can be cited as significant research areas when examining infrastructure systems and infrastructure, as they are inextricably linked to both. Examples include automobile manufacturing, the production of traffic signs, etc. Connecting and utilizing Industry 4.0 technologies and processing simulation solutions to address industry challenges, such as process optimization and fault detection, are gaining in popularity. Cyber-physical systems and digital twins connect the physical and cyber worlds to enable intelligent manufacturing capabilities, increased system flexibility, decreased manufacturing-cycle times, and improved quality. This paper presents a solution that improves the synchronization between the real (physical) and simulation (digital) layers, using discrete-event-driven simulations to create more efficient and accurate digital-twin environments. Using a combination of inexpensive commercial microcontrollers and an inertial-measurement-unit sensor to enhance a standard programmable logic controller process, a discrete-event-simulation-based digital layer is updated in real time to produce a live digital twin. The system can accurately identify and track products throughout the production cycle while simultaneously updating the digital twin in real time. Even independently, the algorithm running on the microcontroller can be used to gather the input parameters required for the simulation of production processes. The implemented environment can serve as a suitable testing ground for investigating the practical applicability of digital-twin solutions.

Automated generation of digital twin for a built environment using scan and object detection as input for production planning

The simulation of production processes using a digital twin can be utilized for prospective planning, analysis of existing systems or process-parallel monitoring. In all cases, the digital twin offers manufacturing companies room for improvement in production and logistics processes leading to cost savings. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a digital twin in a built environment is cost-, time- and resource-intensive and IT expertise is required. These obstacles will be overcome by generating a digital twin using a scan of the shop floor and subsequent object recognition. This paper describes the approach with multiple steps, parameters, and data which must be acquired in order to generate a digital twin automatically. It is also shown how the data is processed to generate the digital twin and how object recognition is integrated into it. An overview of the entire process chain is given as well as results in an application case.

Use of production process simulation as a verification tool

This article deals with the use of manufacturing process simulation as a verification tool. Simulation proves to be a valuable tool for testing and evaluating production processes and their effectiveness in a controlled virtual environment. The authors discuss the advantages that simulation brings, such as the identification of defects, the optimization of production parameters, and the prevention of problems even before the start of the real production process. The use of simulation of the production process enables the acquisition of valuable knowledge about the influence of various factors on the final product quality, the optimization of production procedures, and the minimization of the risk of errors. The authors emphasize that the simulation provides accurate information that helps in the decision-making process and allows the prediction of possible problems and their solutions before they arise.

Enabling causality learning in smart factories with hierarchical digital twins

Smart factories are complex systems where many different components need to interact and cooperate in order to achieve common goals. In particular, devices must be endowed with the skill of learning how to react in front of evolving situations and unexpected scenarios. In order to develop these capabilities, we argue that systems will need to build an internal, and possibly shared, representation of their operational world that represents causal relations between actions and observed variables. Within this context, digital twins will play a crucial role, by providing the ideal infrastructure for the standardisation and digitisation of the whole industrial process, laying the groundwork for the high-level learning and inference processes. In this paper, we introduce a novel hierarchical architecture enabled by digital twins, that can be exploited to build logical abstractions of the overall system, and to learn causal models of the environment directly from data. We implement our vision through a case study of a simulated production process. Our results in that scenario show that Bayesian networks and intervention via do-calculus can be effectively exploited within the proposed architecture to learn interpretable models of the environment. Moreover, we evaluate how the use of digital twins has a strong impact on the reduction of the physical complexity perceived by external applications.

Process Modeling and Simulation of Ammonia Production from Natural Gas: Control and Response Analysis

Optimal production of ammonia (NH3) using natural gas is necessary in order to make it available for wide range of applications including the manufacture of fertilizers, fuel for transportation and during synthesis of some chemicals. Achieving this would require strategic implementation of a control scheme to simulated ammonia production, capable of ensuring adequate realization of production targets. The work involves ASPEN Plus modeling, simulation, sensitivity analysis and control of NH3 production process. Steam/carbon ratio, conversion of CH4, removal of carbon dioxide (CO2) and carbon monoxide (CO), hydrogen/nitrogen ratio and heat exchanger and separator temperatures were identified as requiring control in units any of these specifically impacts. As a result, approximately 176 tons of NH3 was realized daily based on the simulation results and can be scaled-up using a calculated factor equivalent to 1.1375 to 200 tons/day capacity, in this design. Sensitivity analysis resulting in control of certain unit parameters is effective in ensuring process safety, maximum yield of important end-products and reduction in the cost of operation

Production-Process Simulation and Life-Cycle Assessment of Metakaolin as Supplementary Cementitious Material

An environmental assessment of metakaolin as a supplementary cementitious material (SCM) through an integrated production-process-simulation and Life-Cycle-Assessment (LCA) approach is presented in this work. Initially, process simulation models were developed to reproduce the basic stages of the metakaolin production process. The effect of various operational parameters and scenarios, such as calcination temperature, moisture of raw material and associated drying, exhaust gas recirculation and the use of alternative-fuel combustion to provide kiln heat requirements, was evaluated. The resulting process heat-demand and CO2-emission computations were used as inputs in the LCA along with upstream literature data using a cradle-to-gate approach. LCA results focused on the most relevant environmental impact category of cement production, the Global Warming Potential (GWP (100)). The major findings showed a strong influence of process temperature and kaolin humidity on the lifecycle GWP, since both parameters affected not only the core-process heat demand but also the upstream impact related to fossil-fuel extraction, processing, transportation and distribution. Recirculating the exhaust provided a GWP reduction potential of up to 19%. In all examined production scenarios, metakaolin depicted a lower Global Warming Potential compared to clinker due to the avoidance of emissions related to limestone calcination. As regards the impact contribution of fuels, coal was responsible for higher onsite emissions and natural gas indicated higher upstream emissions. The GWP (100) could be further reduced when alternative waste fuels such as plastic waste, MSW (municipal solid waste) and tires were used. The LCA results have been cross-checked with previous literature reports, and the corresponding deviations are accordingly explained. In any case, the LCA results of different studies are rarely directly comparable due to the numerous assumptions required, which cannot be identically replicated.

Simulation of production processes and associated costs in mining using the Monte Carlo method

The application of the Monte Carlo technique to production planning and everyday economic decisionmaking in mine production management is demonstrated. The logic is detailed using an example of underground production with continuous miners (CMs) and truck haulage. It is argued that availability of equipment and personnel are the predominant variables influencing mine output and productivity and that those availabilities may be well represented by binomial probability distributions. The probabilistic model is implemented in a standard Excel® spreadsheet with Palisade's @Risk add-on to facilitate simulations. Starting from model calibration against data obtained from a mine's annual reports, some general interdependencies of availability, utilization, productivity, and costs of production processes are outlined. Finally, several possible options and their consequences as regards production improvements are explored.

A room of errors simulation to improve pharmacy operators' knowledge of cytotoxic drug production.

We used an educational healthcare simulation tool called room of errors (ROE) to raise pharmacy operators' awareness of potential errors in a chemotherapy production process and assessed its impact on their knowledge and satisfaction. Twenty-five errors (compiled from internal procedures, literature and our hospital's reported incidents) were categorised as static (n = 7, visible by the participant anytime) and dynamic (n = 18, made by a pseudooperator in front of the participant). Our simulated cytotoxic production unit (CPU) hosted the 1 h-simulation. Two pharmacists (supervisor/pseudo-operator) welcomed the trainee for a 10-min briefing. During the 20-min simulation, participants watched the pseudo-operator's gestures in a simulated chemotherapy production process. Participants called out each error observed (recorded by the supervisor). A 20-min debriefing followed. ROE's impact on knowledge was measured through participants' answers to a before-and after 18-item questionnaire about CPU's procedures and certainty about answers on a scale (0%-100%). Participants evaluated the training using a satisfaction questionnaire (Likert scale, 1-6). The 14 participants detected 70.4% ± 11.4% of errors. Least-detected errors were "using non-disinfected vials" (42.9%) and "touching syringe plunger" (0%). Critical errors (expired leftovers or glucose instead of sodium chloride) were detected at 57.1%. Knowledge improved from 60.3% to 94.1% (p < 0.001) and certainty from 75.3% to 98.8% (p < 0.001). Participants appreciated this non-judgmental, informative, and original training (satisfaction 95.7%). Some pointed out difficulties settling into the game quickly and visualising static and dynamic errors simultaneously. This ROE simulation improved operators' knowledge and certainty. Longer-term testing should be done to measure knowledge retention over time.