Simulation Of Chemical Processes Research Articles

Integrating a Fischer Tropsch process into a pulp mill - A techno-economic assessment

To reduce the greenhouse gas emissions of the transport sector, synthetic fuels based on CO2 and H2 are one viable option. This paper evaluates the integration of a Fischer Tropsch process into a pulp mill to improve efficiency and to lower CO2 emissions. Different syngas preparation technologies, plant sizes, and gas loop designs are considered. The processes were simulated with a chemical process simulation software to retrieve mass and energy balances. Based on the simulations, the processes are evaluated regarding economics, CO2 emissions, and efficiency indicators.The Power-to-Fuel efficiency reaches almost 40 % for closed loop configuration with reverse water gas shift reactor for carbon monoxide production. For the open loop configuration the efficiency is tremendously lower. In closed loop configurations, the carbon efficiency reaches 80 %. The levelized cost of syncrude ranges from 3.28 to 7.69 €/kg. The most important impact on syncrude price comes from electricity price, investment cost, and efficiency of water electrolysis. Integrating the Fischer Tropsch process into the pulp mill shows advantages in terms of Power-to-Fuel efficiency and CO2 emissions but not necessarily in terms of economics.

Modelling and simulation of fast pyrolysis of pomace from three-phase olive mill targeting optimal yields of pyrolysis products

Biomass can be transformed into useful bioproducts (biofuels, biomaterials) through its thermochemical conversion. This study concerns modeling and simulation of fast pyrolysis of olive pomace from three-phase olive mill using Super Pro Designer (SPD) software, which was selected because of its vast databank of specific chemical compounds as well as specific unit operations specifically designed for modeling and simulation of biological, physical and chemical processes. The simulation was carried out at pyrolysis temperatures ranging from 400 to 675 °C and residence times varying between 0.1 and 15s. Simulation results indicate that fast pyrolysis yielded maximum bio-oil yield of 23.72% at 650 °C and a residence time of 0.1s, and maximum syngas yield of 41.17% at 675 °C and a residence time of 15s. Predicted product yields were in accordance with experimental data collected from the literature, with relative errors in the range of 9%, which may be due to variable feedstock properties. The developed model provides very useful information on olive pomace fast pyrolysis conditions.

Research on the Optimization of the Operating Parameters of Methane Carbon Dioxide Reforming Using the Response Surface Methodology

In order to reduce the production cost of the methane carbon dioxide reforming reaction, and improve its actual production efficiency, in this paper, the optimal working parameters of the methane carbon dioxide reforming reaction are studied. The influence of different factors on methane conversion is studied via a single-factor numerical simulation analysis and the response surface methodology. Firstly, a numerical model of the methane carbon dioxide reforming reaction is established using Ansys Chemkin Pro software to analyze the influence of single factors (reactor temperature, reaction pressure, gas velocity) on methane conversion rate; secondly, the response surface model with the methane conversion rate as the response value is established using the BBD (Box–Behnken design) method; and finally, the order of influence of each variable on methane conversion and the optimal reaction conditions are determined using the response surface method. The factors are listed in order of their influence on methane conversion as follows: reactor temperature > pressure > speed. The results show that when the temperature is 1135.114 K, the pressure is 0.103 MPa and the speed is 10slpm, the methane conversion rate is 93.7018%. In this paper, a method is adopted in which chemical reaction process simulation and numerical results prediction are combined, significantly reducing the simulation time and improving the calculation efficiency and accuracy, thus being of considerable scientific significance and theoretical value.

Hybrid solder joints: the effect of nanosized ZrO2 particles on morphology of as-reflowed and thermally aged Sn–3.5Ag solder joints

The main number of current researches has been focused on the microstructure and mechanical properties of the Sn-based Sn–Ag–Cu-based solders, while various kinds of nanosized particles have been added. The synthesis and handling of ceramic nanosized powder are much easier than of metal nanoparticles. In addition, metal nanoparticles solved in solder joints during the soldering process or by thermal aging could behave as an alloying element similar to bulk metal additions, while ceramic nanoparticles retain their chemically inactive behavior in various thermal, thermo-mechanical, and electrical constraints. In some cases, the solved metal nanosized inclusions could increase the growth kinetics of the present intermetallic phases or even create new phases, which leads to more complexity in the predictions and simulations of chemical processes in the solder joints. Based on the assertions mentioned above, ceramic nanosized particles are industrially more favorable as reinforcing inclusions. On the other hand, there is no direct comparison in the literature between Sn-based Sn–Ag–Cu and Sn–Ag solder joints with similar ceramic nanoinclusions based on microstructural features and mechanical properties. In the present research, the Cu/flux + NPs/SAC/flux + NPs/Cu solder joints were produced with a nominal amount of 0.2 wt%, 0.5 wt%, and 1.0 wt% nanosized ZrO2 powder. The solder joints prepared via the above-described method are called in the literature as hybrid solder joints. The microstructure of the as-reflowed and thermally aged samples has been studied, especially at the interface solder/substrate. It has been shown that the minor additions of ZrO2 NPs lead to a decrease in the thickness of the Cu6Sn5 interfacial layer in the as-reflowed solder joints and a reduction in the growth kinetics of this layer, while the Cu3Sn interfacial IMC layer remains practically unaffected. Similar investigations were performed in our previous study but for both the hybrid and nanocomposite Sn–3.0Ag–0.5Cu solder joints. A comparative analysis of the impact of the ZrO2 nanoinclusions on the hybrid solder joints using Sn–3.5Ag and Sn–3.0Ag–0.5Cu has been performed.

Numerical simulation modelling on whole municipal solid waste incineration process by coupling multiple software for the analysis of grate speed and air volume ratio

Uncertainties in the calorific value and composition of municipal solid waste (MSW), the variability in operations, equipment performance decline, maintenance needs, and other dynamic factors make it challenging for operational experts to accurately gauge the overall combustion impact under fluctuating conditions. There is a need for a visual numerical simulation of the full MSW incineration (MSWI) process, with a focus on the effectiveness of the grate speed and air volume ratio. Unfortunately, software like Aspen Plus struggles to visually depict the effect of typically manipulated variables such as grate speed, and it is not well-suited for simulating solid-phase combustion and flue gas treatment. In response to these challenges, our study introduces a numerical simulation method for the MSWI process using a multi-software coupling approach. This simulation method incorporates (a) the use of custom software for simulating solid-phase combustion on the grate, (b) the application of Computational Fluid Dynamics (CFD) software for gas-phase combustion in the furnace, and (c) the utilization of chemical process simulation software for non-grate solid-phase combustion. The benchmark condition was determined using real-time running data from the Beijing MSWI Power Plant. The results show that for the incinerator’s third flue, flue gas G1, and flue gas G3, the relative errors between the simulated and actual values for flue gas temperature and oxygen concentration were 0.3% and 1.1%, 3.5% and 20%, and 0.2% and 20% respectively. In addition, we used eight non-benchmark conditions to study the effects of grate speed and air volume ratio. When the grate speed was gradually increased from 6 m/h to 8 m/h, O2, CO2, sulfur dioxide (SO2), sulfur trioxide(SO3), nitrogen monoxide (NO), and nitrogen oxide (NO2) at G1 had error fluctuation ranges of 5.9%− 48.5%, 2.5%− 11.5%, 10.42–28.57%, 7–23%, 29.10–45.52%, and 24.39–48.78%, respectively. When the primary air ratio was gradually increased from 0.84 to 0.96, the O2 concentration at G1 exhibited a steady upward trend. The simulation results provide insights into the adjustment range of the manipulated variables and offer additional support for the visualized and optimized control operation of the MSWI plant.

Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components

The determination of liquid phase equilibria plays an important role in chemical process simulation. This work presents a generalization of an approach called the convex envelope method (CEM), which constructs all liquid phase equilibria over the whole composition space for a given system with an arbitrary number of components. For this matter, the composition space is discretized and the convex envelope of the Gibbs energy graph is computed. Employing the tangent plane criterion, all liquid phase equilibria can be determined in a robust way. The generalized CEM is described within a mathematical framework and it is shown to work numerically with various examples of up to six components from the literature.

Transdisciplinary competency-based development in the process engineering subjects: A case study in Brazil

Recently, the Brazilian Ministry of Education issued New Curriculum Guidelines for engineering programs. This paper encompasses a pedagogical intervention reflecting our efforts to incorporate these new guidelines into our engineering program. Specifically, this work has led to the competency-based rework of the following subjects offered in the Chemical Engineering Undergraduate Program at the Federal University of São Paulo (Unifesp): I) Modeling and Systems Analysis; II) Synthesis and Optimization of Chemical Processes; III) Chemical Process Simulation; IV) Process Analysis and Control; V) Chemical Process Design; and VI) Chemical Installations Design. Thirteen transdisciplinary competencies are integrated throughout the six subjects. Students highlighted design thinking, lifelong knowledge/learning, openness to act autonomously, teamwork, communication, and cooperation as essential qualities. Moreover, the greater focus on the process systems engineering approach involving the analysis, synthesis, design, and control of sustainable processes helps chemical engineers to face new challenges using renewable resources.

ПРОГНОЗИРОВАНИЕ СВОЙСТВ И МОДЕЛИРОВАНИЕ ФАЗОВОГО СОСТОЯНИЯ МНОГОКОМПОНЕНТНЫХ СИСТЕМ НА ЯЗЫКЕ PYTHON

The thesis considers an approach to compiling a mathematical simulation of chemical processes using the Python programming language. General information about the purpose and capabilities of the ‘Thermo’ and ‘Chemical’ modules is given. It is shown that these modules contain the main functions and classes that make it possible to predict the properties and to simulate the phase states of multicomponent systems

Solvent Induced Proton Polarization within the Nuclear−Electronic Orbital Framework

To explicitly account for nuclear quantum effects and solvent environments in simulations of chemical processes, the nuclear-electronic orbital approach is coupled with a polarizable continuum model (PCM). This NEO-PCM approach is used to explore the influence of solvation on nuclear polarization through applications to a water dimer and a set of protonated water tetramers. Nuclear polarization in these species is analyzed in terms of changes in proton density and oxygen-hydrogen bond length. Solvation is shown to enhance nuclear polarization with increasing dielectric constant. For the water dimer, the internal, hydrogen-bonded proton is shown to polarize more than the external, free proton. Moreover, proton quantization leads to greater solvent polarization through their mutual polarization. These calculations highlight the complex interplay among electronic, nuclear, and solvent polarization in chemical systems.

Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data

Long short-term memory neural networks have been proposed as a means of creating accurate models from large time series data originating from various fields. These models can further be utilized for prediction, control, or anomaly-detection algorithms. However, finding the optimal hyperparameters to maximize different performance criteria remains a challenge for both novice and experienced users. Hyperparameter optimization algorithms can often be a resource-intensive and time-consuming task, particularly when the impact of the hyperparameters on the performance of the neural network is not comprehended or known. Teacher forcing denotes a procedure that involves feeding the ground truth output from the previous time-step as input to the current time-step during training, while during testing feeding back the predicted values. This paper presents a comprehensive examination of the impact of hyperparameters on long short-term neural networks, with and without teacher forcing, on prediction performance. The study includes testing long short-term memory neural networks, with two variations of teacher forcing, in two prediction modes, using two configurations (i.e., multi-input single-output and multi-input multi-output) on a well-known chemical process simulation dataset. Furthermore, this paper demonstrates the applicability of a long short-term memory neural network with a modified teacher forcing approach in a process state monitoring system. Over 100,000 experiments were conducted with varying hyperparameters and in multiple neural network operation modes, revealing the direct impact of each tested hyperparameter on the training and testing procedures.

Modeling of radiation heat transfer in dense-bed flows of solids in indirectly heated rotary kilns

• modification and implementation of a DOM-radiation model which combines all required physical submodels to simulate heat radiation to dense beds of particles. • Full time and space resolved simulation of thermal conversion of wet moving bulk in a large-scale rotary kiln reactor. • Analytical validation of model and justification with laboratory-scale experiments. • Efficiency optimization of the numerical solver for industrial-scale and long-time simulations on parallel supercomputers. • Parameter studies for large-scale reactors to investigate conversion rate and product quality. This work presents further development and validation of the Discrete Ordinates Model for thermal radiation implemented in OpenFOAM® for application to packed beds of particles. The radiation model is an important part of a more comprehensive model for simulating the thermochemical conversion of the discrete phase (here for instance wet biomass particles). The comprehensive Eulerian-Lagrangian model is part of a three-dimensional, time-resolved simulation of the essential chemical and physical processes occurring within and in-between particles in a moving bed. For the thermal treatment of solid particles, convective and radiative heat transfer couple the energy exchange between the reactor wall, gas- and disperse phase. The original implementation of the finite volume Discrete Ordinate Model (fvDOM) present in OpenFOAM® is valid for a dilute particulate phase and neglects the effect of local opacity due to the existence of individual particles. In the present application, a dense-packed bed of the particulate phase exists in the reactor and, therefore, this direction-based radiation model is adjusted for a computational cell with arbitrary particle volume fractions. To validate the results with the present thermal radiation model, a simple test case is first presented, where a bed of particles is heated from the top of the computational domain. A second test case relates to an experimentally investigated laboratory-scale reactor. The results of the improved fvDOM are compared to the original implementation in OpenFOAM® and the more simple and computationally cheap P-1 radiation model. In the presence of a packed bed, the P-1 model largely overpredicts the radiative heat transfer while the original fvDOM underpredicts the heat flux by about 15 % for the first test case. The new improved model delivers results within 1% deviation at the expense of maximum of 10 % increase in the computational time. Large-scale parallel simulations of real setups are crucial tools to predict the efficiency of a process and improve the operational parameters. To achieve the pre-defined product quality at minimum cost, an example of the implemented radiation model in thermochemical conversion of wet biomass in a rotary drum is been given and the importance of the radiation heat transport to the bulk is highlighted.

50 years of Soave Equation of State (SRK): A source of inspiration for chemical engineers

In June 1972, Giorgio Soave published a renown scientific paper (Equilibrium constants from a modified Redlich-Kwong equation of state, Chemical Engineering Science, 27, 1197-1203), which is one of the most ever cited papers in the field of Chemical Engineering. In the 50th anniversary of this publication it seems appropriate to give a tribute to the Author, as well as quite interesting to review the revolutionary effect that his idea brought not only in chemical thermodynamics but also in the simulation, analysis and design of chemical processes. In this paper the development of the cubic Soave-Redlich-Kwong equation of state is critically summarized, and its applicability to evaluate fluid phase equilibria and chemical equilibria is discussed and explained with examples. It is also shown that the accuracy of cubic equations of state in calculating fluid phase properties mainly depends on the way their parameters are evaluated, rather than on the type of equation of state. It is concluded that still nowadays a cubic equation of state is an essential tool to deal with complex mixtures when accurate calculations are needed, provided that the required data, as well as the model limitations, are adequately considered, with just a couple of impossible-to-solve limitations. In addition, it is eventually pointed out that no more improvements to cubic equations of state are to be expected after Soave's work.

Exergy study of hydrogen cogeneration and seawater desalination coupled to the HTR-PM nuclear reactor

This work presents a novel integration system of the high-temperature gas-cooled reactor-pebble bed module project to a hydrogen production process using the iodine-sulfur cycle in cogeneration with seawater desalination. The current approach includes a Rankine cycle, a sulfur-iodine thermochemical cycle for hydrogen production and a multi-stage flash desalination process. The use of a catalyst that allows the H2SO4 decomposition reaction to being carried out at temperatures compatible with the nuclear reactor project is considered. The residual heat from the acid decomposition reactions is used to desalinate seawater through the multi-stage flash process. A chemical process simulator is used to create a computational model that allows estimates of global and local efficiencies of the proposed flow diagram. Some operating parameters were sized, and their influence on the efficiency is also reported. The proposed model for the sulfur-iodine cycle can produce 0.41 kg/s of hydrogen with partial energy and exergetic efficiency of 37.35% and 38.64%. The desalination process can process 40.70 kg/s with energy and exergy efficiencies of 58.78% and 82.66%, respectively. The higher exergy destruction share is obtained in the heat exchangers (36.55%), chemical reactors (16.56%) and separators (12.80%). The global system showed efficiencies of 40.13% and 52.04%, respectively.

Kinetic modeling and thermodynamic analysis of toluene disproportionation reaction over ZSM-5 based catalysts

The aim of this work was to carry out an experimental study along with kinetic and thermodynamic analysis for the toluene disproportionation reaction under actual industrial operating conditions. The toluene disproportionation reaction was carried out under pressurized hydrogen and over three different ZSM-5 based catalysts, namely, ZSM-5, 3%Mo on ZSM-5, and 3%Mo on ZSM-5/Mordenite. The influence of reaction temperature and catalyst composition on conversion and product distribution was investigated. A network of reversible reactions and elementary rate laws reflecting the product distribution, including xylene isomers and other products, were proposed and validated using the obtained experimental data. The thermodynamic parameters were estimated using a chemical process simulation package (Aspen HYSYS), while numerical and statistical methods were used to estimate the kinetic parameters. A notable agreement between the model predictions and the experimental data was obtained. The Mo-impregnated, mordenite-containing catalyst (3%Mo on ZSM-5/Mordenite) was found to be the most active catalyst, presumably due to the additional metallic-active sites that were readily accessible through the mordenite's large pores compared to the plain ZSM-5 catalyst. In addition, the most active catalyst was found to have the lowest activation energies and lead to conversions that were limited thermodynamically.

Meeting sustainable aviation fuel policy targets through first generation corn biorefineries

This paper investigates options to produce low carbon and renewable aviation fuel through existing commercial first generation biorefineries, which could be strategic given the rapid growth of the electric vehicle market for personal mobility. We evaluate the environmental impact of retrofitting a first generation biorefinery to produce aviation fuel through chemical process simulation. The analysis investigates the potential for reducing greenhouse gas (GHG) emissions and displacing fossil fuel (petroleum-based jet kerosene) in the aviation fuel industry through using corn feedstocks in dry grind biorefineries to convert sugars to 1,4-dimethylcyclooctane (DMCO), which qualifies as renewable jet-A fuel blend and is infrastructure compatible. The average life cycle GHG emissions for a baseline case and a scenario with carbon capture and storage (CCS) of fermentative CO2 are 36 and 5 g CO2 e/MJ DMCO, respectively. Investment in CCS at the biorefinery and adoption of crop best management practices on farms are essential for mitigating the risks of induced land use change GHG emissions for existing corn biorefineries. DMCO as a low-carbon aviation fuel could be a strategic fuel product for retrofitting existing corn dry grind facilities. For aviation fuel supply, corn dry grind facilities could displace about 12 % of jet fuel demand in the near term, which would meet sustainable aviation fuel policy targets. We conclude that corn-to-DMCO shows promise for near-term low-carbon fuel markets in the aviation industry and as a bridge to developing biomass-based jet fuel in the future.

Design Optimization of Highly Efficient SOEC Co-Electrolysis Processes

Introduction Research is underway to produce synthetic fuels such as hydrocarbon fuels by reacting hydrogen derived from renewable energy with carbon dioxide and other substances, and their use towards a decarbonized society is being considered.1 In this study, we focus on high-temperature co-electrolysis of CO2 and H2O using a solid oxide electrolysis cell (SOEC), which is one of the promising CO2 conversion technologies. The objective of this study is to evaluate the influence of the electrolysis process and operating conditions on the fuel synthesis process based on heat and mass balance analysis and to propose designs for a new fuel production system. Experimental A commercial chemical process simulation software Aspen Plus was used to simulate a fuel synthesis process using SOEC high-temperature co-electrolysis. In this simulation, H2O and CO2 were considered to be supplied to the SOEC for co-electrolysis, and a fuel synthesis process such as a CH4 production reactor was connected to the outlet of the SOEC to calculate the overall system efficiency. Figure 1 describes an SOEC and a fuel synthesis reactor of the SOEC high-temperature co-electrolysis process created by Aspen Plus. Results and discussion Figure 2 shows one of the simulation results calculated using the process model shown in Figure 1, where the operating temperature of the SOEC is 800 ℃ and the reaction pressure is 1 bar. Figure 2 shows the change in gas composition after the co-electrolysis process in the SOEC by varying H2O flow rate from 1 to 5 mol min-1 relative to CO2 flow rate of 1 mol min-1. As shown in Figure 2, the conversion rate of CO2 to CO increases with increasing H2O flow rate due to the reverse water-gas shift reaction, and the ratio of H2O to CO peaked at H2O/CO2=2, and then gradually decreased. Since the ratio of these gases affects the efficiency of fuel synthesis to CH4 and other downstream gases, we have studied the appropriate fuel synthesis ratio based on the simulation results. In this presentation, we report on the results of the performance evaluation of co-electrolysis and fuel synthesis process based on the obtained simulation results, by conducting electrolysis tests using SOEC cells and evaluating cell performance and downstream gas composition under different operating conditions, based on actual experimental results. Acknowledgements This work was partially supported by JSPS KAKENHI Grant Number JP21K04981. Figure 1

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.