Simulation Of Chemical Processes Research Articles

Comparison of open- and closed-loop operating strategies for exhaust gas scrubbing in marine applications: Modeling and sea-trial data

A model of a hybrid marine engine exhaust gas scrubber is constructed using sea-trial data and a chemical process simulator. The model can describe the performance of the scrubber under both open-loop and closed-loop operation. Predictions for solubility and reaction equilibria are validated using published data. The model results are consistent with the sea-trial data. Sensitivity analyses are conducted to determine the effect of various operating parameters on the scrubbing performance for both open-loop and closed-loop operation.

Diffusivities of linear unsaturated ketones and aldehydes in compressed liquid ethanol

For the accurate design, optimization and simulation of chemical processes limited by mass transfer kinetics it is important the knowledge of transport properties, namely, diffusion coefficients, D12. In this work, the D12 values of six unsaturated linear ketones (i.e., propanone, butanone, propan-2-one, propan-3-one, hexan-2-one and hexan-3-one) and three unsaturated linear aldehydes (i.e., butanal, pentanal and hexanal) in (compressed) liquid ethanol were measured at temperatures from 303.15 K to 333.15 K and pressures up to 150 bar. The D12 values of ketones are in the range of 1.28 × 10−5 – 2.89 × 10−5 cm2 s−1 and of the aldehydes are between 1.39 × 10−5 and 2.68 × 10−5 cm2 s−1. The general trends of D12 regarding temperature, pressure, Stokes-Einstein coordinate, and free volume are presented and discussed. The diffusivities of the various ketones position isomers and aldehyde/ketone isomers were statistically compared, being possible to conclude that the former ones exhibit indistinguishable diffusivities while different values appear for aldehydes/ketones isomers. Finally, five models and a machine learning algorithm from the literature were tested to predict/correlate the new data. It is suggested that the TLSM model should be the preferred approach for D12 prediction of linear unsaturated aldehydes and ketones in liquid compressed ethanol.

USING ASPEN PLUS TO SIMULATE PHARMACEUTICAL PROCESSES – AN ASPIRIN CASE STUDY

Objective: The objective of this paper is to illustrate uses of Aspen Plus (Aspen) to pharmaceutical processes with a specific focus on the production of aspirin. Chemical process simulators such as Aspen have received little attention for pharmaceutical applications; this is due in part to prevalence of dynamic batch reactors, specialized raw materials and products often including solids and solids handling unit operations. Methods: Aspen was used to first validate an experimental study and then extended to a commercial scale process. Results: Aspen adequately reproduced the experimental results obtained from a dynamic batch reactor. Extension to the commercial scale illustrated the power of Aspen to simulate pharmaceutical processes as well as provide costing and economic analysis. Conclusions: It was found that although the modeling of this relatively simple process is more complicated than it initially seemed, Aspen was capable of handling the difficulties inherent in dealing with solids, batch reactions, and crystal growth. In addition, its optimization and economic analysis features provided enhanced flow sheeting functionality. Its batch reactor model, RBATCH, is capable of modeling batch reactors involving multiple solid-liquid reactions following various reaction rate laws.

An Improved Convolutional Neural Network for Recognition of Incipient Faults

In order to overcome the shortcomings that regular recognition methods do not extract the pivotal difference information of incipient faults with overlapped features, two optimized methods are used to enhance the learning ability of extractor and promote the recognition accuracy of classifier. Firstly, based on one-dimensional neural network (1D-CNN) and its variant temporal convolutional network (TCN), an improved network structure called temporal convolutional block-convolutional neural network is proposed. It learns historical information by causal convolution and general trend information by non-causal convolution. Secondly, a new loss item named direct discriminant is proposed to optimize the loss function based on the softmax classifier. It makes classifier inputs have better separability. Compared with existing recognition methods such as variational modal decomposition-stochastic configuration network (VMD-SCN), manifold regularized stochastic configuration network (MRSCN), kernel principal component analysis-deep belief network (KPCA-DBN) and TCN, the superior recognition of the proposed method is illustrated through a numerical example and TE chemical industrial process simulation.

Process Simulation of Steam Stripping of Bleached Palm Oil Deodorization for Removing Free Fatty Acids using DWSIM

The presence of free fatty acids (FFAs) and odoriferous components in the bleached palm oil (BPO) need to be removed to meet the specific standard quality and make the oil palatable. Because FFAs and odoriferous components is more volatile than the main oil components, they vaporize readily thus being removed from the oil product. Deodorization is the key process in industry to remove FFAs by vaporizing them using steam stripping agent such as steam under vacuum. In this work, a simulation study was adopted for the analysis of deodorization process implemented in an open-source chemical process simulator software, DWSIM. The deodorization section was modelled using absorber column unit operation in the software. The process conditions and BPO compositions fed into the deodorizer were taken from literature. The simulation showed much decrease in the FFAs content below the maximum value as standardized by the authorized association. The deodorization of BPO was analyzed by studying the column profiles such as the individual TGs, FFAs and water interstage flows in vapour and liquid phases, and the total component interstage flow for vapour and liquid phases’ profiles. From the simulation study under defined process conditions, the percentage removal of FFAs from the BPO was >99.99%.

Economy, Exergy, energy consumption and environmental human toxicity potential assessment of vacuum extractive distillation coupled pervaporation process for separating Acetone/Isopropanol/Water Multi-azeotropes system

Industrial wastewater from the catalytic hydrogenation of acetone to isopropanol contains multi-azeotropes. It is difficult to purify acetone and isopropanol by traditional distillation, and the treatment capacity and potential economic benefits are huge. The process design of the sustainable development of vacuum extractive distillation coupled with pervaporation technology to separate the industrial organic wastewater from ACT hydrogenation to IPA has changed the sequence of separation of the multi-azeotrope system with sustainable development and reduced the energy consumption and energy consumption of the multi-azeotrope separation for resource recovery. The comprehensive economic cost enhances the evaluation of the environmental human toxicity to the wastewater separation and discharge process. Based on the strict design specifications of the chemical system process simulation software, the sequential iterative method is utilized to simulate the design and optimization of the chemical separation process. The EHH2O-EG process achieved a reduction of 7.40% (TAC), 7.51% (energy consumption and GWP), and 7.36% (HTP) compared with the EHEG-H2O process in terms of the economy, exergy, environmental assessment, energy consumption, and human toxicity potential, and EHH2O-EG process is better than EHEG-H2O process. This novel separation process provides a novel idea for process optimization, and is the best designing and separating sustainable process for promoting green chemical wastewater discharge treatment. It has significantly superior instructive for the design of selecting different solvents to separate multi-component azeotropes. The value of this research is investigated competitive and cost-effective in the current chemical industry for the production of rubber additives, bisphenol A and polycarbonate, isopropyl alcohol pharmaceuticals, and cumene to achieve the progressive goal of carbon neutralization.

Emerging Research Trend in Chemical Technology Towards Sustainable Development

The rapid industrialization and urbanization as well as rising global transportation have induced numerous challenges regarding energy security, environmental protection, waste management, and greenhouse gas emissions across the world. Addressing these current issues is crucial to guarantee the sustainable development, green environment, and circular economy. The implementation of advanced chemical technology in industrial production, worldwide transportation, and environmental treatment could contribute to efficiently solve these global challenges. This special issue of Chemical Engineering & Technology highlights the recent research trends in chemical technology towards sustainable development such as advanced material synthesis from various waste sources, biological and thermochemical processes for alternative biofuels and chemicals production. In particular, the synthesis of polysulfide from waste cooking palm oil and the production of hybrid films reinforced with cellulose nanofibers used as packaging materials are thoroughly discussed. Additionally, a comprehensive review regarding waste biomasses transformation to value-added biofuels and biochemicals via thermochemical processes, namely, liquefaction, gasification, torrefaction, and pyrolysis, is presented in this special issue. The environmental benefits associated with these approaches are also underlined. Computer-aided methods including simulation, modeling, and optimization of different chemical engineering processes for energy and environmental applications are offered as well. The contribution from authors across the world to this thematic issue is highly appreciated. The guest editors also acknowledge the valuable effort and constructive comments from reviewers for manuscript assessment. Lastly, we would like to appreciate the Editor in Chief Dr. Barbara Böck and the entire editorial office for their valuable support and guidance during the completion of this topical issue. Dr. Dai-Viet N. Vo, Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, Vietnam Dr. Sumaiya Zainal Abidin, Department of Chemical Engineering, College of Engineering, Universiti Malaysia Pahang, Malaysia Dr. P. Senthil Kumar, Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, India Dr. Muthusamy Govarthanan, Department of Environmental Engineering, Kyungpook National University, South Korea Dai-Viet N. Vo Sumaiya Zainal Abidin P. Senthil Kumar Muthusamy Govarthanan

A Hill Equation for Solid Specific Heat Capacity Calculation

The Hill Equation and Hill Coefficient have been used extensively in biochemistry for the description of noncovalent binding. Previously, the Hill Coefficient was correlated with the Gibbs free energy, which suggests that the Hill Equation might be extensible to covalent binding phenomena. To evaluate this possibility, the Hill Equation was compared to the Debye Model and Einstein Solid in the calculation of heat capacity for 53 covalent solids, which included stainless steels and refractory ceramics. Hill Equation specific heat predictions showed a standard error of 0.37 J/(mole⋅Kelvin), whereas errors from the Debye Model and Einstein Solid were higher at 0.45 J/(mole⋅Kelvin) and 0.81 J/(mole⋅Kelvin), respectively. Furthermore, the Hill Equation is computationally efficient, a feature that can accelerate industrial chemical process simulation(s). Given its speed, simplicity, and accuracy, the Hill Equation likely offers an alternative means of specific heat calculation in chemical process models.

Granger causality procedeture to diagnosis and failture in industrial systems

Industrial process supervision is an important subject now days due to the increased requirement for safer processes for operators and effective for companies. Control loops affected by disturbs, are grouped with PCA, based on their increased variability and the causal relationships between them are detected via Granger causality. A graph drawing algorithm allows indicating the source of the disturbance. The procedure is applied to data from a simulated chemical process CSTR. The proposed procedeture correctly indicated the sources of disturbances.

Continuous Distillation

In the present study, a Distillation process separate two components namely Toluene and Benzene is being performed. The distillation experiments are carried out on the DWSIM software. Where we learn to create a material stream in DWSIM. The types of distillation performed on DWSIM include Shortcut distillation, Rigorous Distillation. Process equipment includes suction scrubber compressor discharge cooler mixture and value the major difference between the booster compression and sales as compression are the operating pressure temperature and chemical composition of gas production the flow sheet of sale gas compression and export in DWISM. We specifically focus on the simulation of chemical process using the modelling software to evaluate thermal and chemical behaviour of the system which uses the chemical processes related to offshore petroleum production facilities as an example to demonstrate the software capabilities of DWSIM.[1].

Multiscale modeling of stochastic dynamics processes with MBN Explorer.

Stochastic dynamics describes processes in complex systems having the probabilistic nature. They can involve very different dynamical systems and occur on very different temporal and spatial scale. This paper discusses the concept of stochastic dynamics and its implementation in the popular program MBN Explorer. Stochastic dynamics in MBN Explorer relies on the Monte Carlo approach and permits simulations of physical, chemical, and biological processes. The paper presents the basic theoretical concepts underlying stochastic dynamics implementation and provides several examples highlighting its applicability to different systems, such as diffusing proteins seeking an anchor point on a cell membrane, deposition of nanoparticles on a surface leading to structures with fractal morphologies, and oscillations of compounds in an autocatalytic reaction. The chosen examples illustrate the diversity of applications that can be modeled by means of stochastic dynamics with MBN Explorer.

Process Simulation-based Net Energy Analysis for Future Bioethanol Production as Commercial Biofuel from Waste Rice Straw in Sri Lanka

As the staple food crop in Sri Lanka, paddy rice occupies around 34% (over 0.87 million hectares of land) of the total arable area in the country, corresponding to an average rice production of 3,774,344 t/year. Rice straw is the major biomass waste from rice cultivation, which approximates to an average of 2,830,758 t/year generation at a theoretical straw/grain ratio of 0.75. Open burning of rice straw in paddy fields is the common practice, which could result in an average GHG emissions of 92 kg CO2 eq/t of dry rice straw and other harmful airborne emissions. Application of rice straw into soil as an organic fertiliser is also an inefficient practice, compared to bioenergy generation using rice straw. The average composition of the Sri Lankan rice straw (i.e. 30.0 wt.% cellulose, 3.9 wt.% hemicellulose, 38.2% lignin, 16.1 wt.% wax, and 12.3 wt.% silica) shows the possibility to be used as a second-generation bioethanol feedstock. Existing studies indicate that bioethanol production from rice straw is more environmentally-benign, compared to alternative options, such as gasification for combined heat and power and dimethyl ether (DME) production. This study analyses the net energy indicators of a possible bioethanol production process from rice straw in Sri Lanka. Chemical process simulations using Aspen Plus software were utilised to evaluate the bioethanol production process from rice straw, with a plant output capacity of 1,000 litres/hr of dehydrated bioethanol (99.7 vol.% ethanol) that can be blended with gasoline as a commercial fuel (e.g. E10: 10% bioethanol+gasoline) without any vehicle engine modification. The cradle to gate bioethanol production process from waste rice straw, considered for net energy analysis consists of three major stages: 1. Rice straw preparation, 2. Rice straw transportation, and 3. Bioethanol conversion. The results show that the considered bioethanol production process has a positive net energy gain and increased renewability factor. Detailed analysis indicates that only around 8% of the total process energy consumption is utilised for the bioethanol dehydration operation that is favourable for converting any existing rice straw ethanol plant into commercial gasohol production plant. The sensitivity of bioethanol yield and process energy parameters for the net energy indicator results are further analysed and discussed. The findings from this study can support decision making for a future waste-to-biofuel plant using waste rice straw in Sri Lanka.Keywords: Rice straw, Bioethanol production, Net energy analysis, Process simulation, Waste-to-biofuel

Evaluation of an Open-source Chemical Process Simulator Using a Plant-wide Oil and Gas Separation Plant Flowsheet Model as Basis

In this paper, a detailed evaluation of the open source process simulator DWSIM is presented. Using a previously published simulation model of an oil and gas separation plant, the results obtained with DWSIM are compared to a commercial process simulator widely used in the industry. The modelled flow scheme comprises a vast number of unit operations including separators (flash vessels), valves, splitters, mixers, compressors, heat exchangers, pumps and recycles (tear streams). The results obtained with DWSIM both for characterization of the inlet fluid as well as for a single operating state for the entire process, compare very well with the data obtained using a commercial tool. A rigorous comparison is made and generally, compared results are within 1% in deviation with a few exceptions. Further, an elaborate comparison is made for over 90 simulations with different settings where 10 independent variables are randomly varied over a wide range. Again, good agreement is found between the two tools. The results are very encouraging and provide fidelity in the use of the investigated open source process simulation tools in a professional environment.

Reliability-Based Robust Multi-Objective Optimization (RBRMOO) of Chemical Process Systems: A Case Study of TEG Dehydration Plant

Natural gas (NG) production has significantly increased in the past decade as new unconventional oil and gas wells are being discovered. NG as obtained from the wellhead requires processing before being considered as pipeline grade. The process consists of the removal of acidic gases followed by dehydration. NG processing is associated with toxic emission having substantial environmental and health impact. Difficulty in NG processing arises from varied flow rate and uncertain feed composition that provides a challenge in efficient design as well as finding the optimal operating condition. The present work used a stochastic approach to characterize natural gas composition and its importance on the product and waste emission is studied. Under the uncertain feed composition, optimal operating condition of the controllable variables was attained by a reliability-based robust multi-objective optimization (RBRMOO) technique that mitigates BTEX emission while fulfilling NG pipeline specification. Chemical process simulator is used to find the impact of the control process settings and variation of uncertain feed condition on NG dehydration and BTEX emission. The best prediction models were developed using machine learning algorithm, chosen from a family of metamodels. RBRMOO is performed using metaheuristic algorithm to determine the optimal process condition of the control variables. The impact of uncertain feed composition in process modeling and subsequent optimization demonstrates optimal process condition where the rate of emission is lower by ~83 ton/yr when compared to that from the deterministic model where median value of uncertain feed composition is used for analysis, portraying the limitations of traditional sustainability assessment methods that do not account for uncertainty.

Performance assessment of biogas-fed solid oxide fuel cell system for municipal solid waste treatment

An enormous amount of municipal waste is produced globally and a large proportion of this waste is dumped into the landfill that is now about to reach the maximum capacity and makes it obligatory to investigate alternative methods for waste treatment. This study proposes a novel design to treat the organic fraction municipal solid waste (OFMSW) to generate electricity using solid oxide fuel cells (SOFC). The detailed process simulation is conducted using the standard chemical process simulator Aspen Plus which predicts the electricity that is generated using the OFMSW as feedstock. The biogas from the system is employed to the reformer that produces syngas and this mixture of hydrogen and carbon monoxide is fed to the SOFC with air to generate electricity. The detailed characterization of the proposed system is conducted and numerous sensitivity analyses are performed to investigate the system performance. The study also employs a detailed technical and economic feasibility of the biogas-fed SOFC system for municipal solid waste treatment. The proposed design employs 1,000 Nm3/hr of biogas flow rate that produces 3,633 kW of DC power using SOFC. The thermal management of the designed system is also performed to recover the additional heat within the designed system. A detailed techno-economic feasibility study is conducted for the proposed system and the capital and operating expenditures, income and depreciation costs are also evaluated. The economic and cash flow analyses show that the plant is suitable for a long-term scenario. It has been predicted that if the electricity price increase to 130 $/MWh, it will take 5 years as a payback period for the long-term scenario. Furthermore, the results obtained from the sensitivity studies and techno-economic analysis are presented 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.

Evaluation of hybrid ocean thermal energy conversion system plantwide performance

Abstract Ocean Thermal Energy Conversion (OTEC) is a renewable energy source in which energy is produced by converting the heat stored in the sea or the ocean thermal energy into valuable work, based on the temperature difference between the warm surface seawater and the cold deep seawater. One of the OTEC system requirements is to have a seawater temperature difference at a minimum of 20 °C within a depth of 1000 m below sea level. Recognizing the importance of optimum sea water temperature, several studies have been conducted to optimize the OTEC system. However, none of these studies was attempted under a hybrid ocean thermal energy conversion (H-OTEC) setup. A H-OTEC system is a combination of closed-cycle and open-cycle OTEC system. The objective of this study is to evaluate the performance of the H-OTEC process system based on the impact of seawater temperature variation by simulating H-OTEC process system. Aspen HYSYS was used as a chemical process simulation platform for conducting this study. After the model was completed, verification test was conducted before the simulated data was recorded. The data for the pump work input and the turbine work output were acquired to determine the net power output and system efficiency. The net power output, Carnot efficiency, and thermal efficiency were recorded approximately 1.39 kW, 5.7%, and 1.45%. The data for net power output and the efficiencies of the system was recorded for every 1 °C of increment in surface seawater temperature. The results showed that the net power output increased slightly by 0.5kW, with efficiency difference for both Carnot cycle and actual cycle, recorded to be less than 3% and 0.1% respectively.

A review of the contributions of P. Raj Bishnoi to chemical engineering

AbstractThis paper provides a review of Professor Bishnoi's research contributions in the broad discipline of chemical engineering. Prof. Bishnoi has been a dedicated mentor, collaborator, innovator, and outstanding researcher. He has had a most productive and illustrious career, spanning more than four decades in both academia and industry. He is among the top 50 most‐published authors of the The Canadian Journal of Chemical Engineering. He initiated a most successful and renowned research group at the University of Calgary, which was dedicated to the understanding of thermodynamics and kinetics related to the formation and decomposition of gas hydrates as well as the chemical engineering process design and simulation. As a tribute to Prof. Bishnoi's numerous scientific and engineering contributions and innovations, this review highlights his academic and research accomplishments, which included the training of several doctoral degree students, master's degree students, and postdoctoral scholars and research associates. In this paper, we have divided Prof. Bishnoi's research contributions into three broad areas, namely, chemical thermodynamics of hydrocarbon systems, process development and simulation, and thermodynamics and kinetics of the formation and decomposition of gas hydrates.

Similarity and sparsity collaborative embedding and its application to robust process monitoring

Multivariate measurements in complex industrial processes are commonly contaminated by a number of outliers. In this context, robustness to the corrupted data is an important problem in process monitoring tasks. This paper proposed a novel approach called similarity and sparsity collaborative embedding (SSCE) for efficient robust process monitoring. The proposed SSCE can learn a sparse coefficient matrix by a ℓ1-norm regularization as the sparse constraint on the reconstruction errors, making it robust to the data contaminated by outliers. The similarity preserving matrix is proposed to capture the local structure of the given data, and then the local information is transferred to the sparse coefficients such that the similarity among data points can be preserved. In this way, the reduced-dimensional representations extracted is capable of containing more informative and discriminating characteristics of the original data, which is beneficial to enhance monitoring performance. Meanwhile, projection learning is integrated into the proposed objective function to learn an explicit projection matrix in an overall optimum way, which enables the SSCE to circumvent the out-of-sample problem and facilitate subsequent process monitoring tasks. Two case studies on a simulated typical chemical process and a practical fractionation process demonstrate the effectiveness of the proposed approach.

Optimal experimental design of physical property measurements for optimal chemical process simulations

Chemical process simulations depend on physical properties, which are usually available through property models with parameters estimated from experiments. The required experimental effort can be reduced using the method of Optimal Experimental Design (OED). OED reduces the number of experiments by minimising the expected uncertainty of the estimated parameters. In chemical engineering, however, the main purpose of an experiment is usually not to determine property parameters with minimum uncertainty but to simulate processes accurately. Therefore, this paper presents the OED of physical property measurements resulting in the most accurate process simulations: c-optimal experimental design (c-OED). c-OED aims to minimise the uncertainty of the process simulation results, which is estimated by linear uncertainty propagation from uncertain property parameters through the process model. We use c-OED to design liquid-liquid equilibrium and diffusion experiments minimising thermodynamic and economic performance metrics of three solvent-based processes. In all three case studies, the c-optimal design substantially reduces the experimental effort for the same simulation accuracy compared to state-of-the-art OED that neglects the process. Our findings are confirmed by a Monte-Carlo simulation of the designed experiments. Furthermore, we assess the limits of c-OED for highly nonlinear process models. Thus, the work shows how c-OED can successfully reduce experimental effort required for accurate process simulations by tailoring experimental designs to the process model.