Bibliometric and Co-Occurrence Study of Process System Engineering (PSE) Applied to the Polyvinyl Chloride (PVC) Production

PVC is widely used in packaging, electrical insulation, and medical devices due to its versatility owing to its resistance, incombustible and barrier properties as well as affordable cost. In the present study, bibliometric and co-occurrence analyses are proposed to identify trends, gaps, future directions, and challenges regarding process system engineering (PSE) applied to the production process of PVC using VOSviewer as a tool for analyzing the data obtained from SCOPUS. A mapping of different topics alluding to simulation of PVC production was provided to gain a better insight into the development of the topic and its progression. The findings indicate that the literature on this topic falls into five different clusters: modeling and simulation of PVC production, process control and optimization, and optimization strategies of the process. From a co-occurrence study we identified that mathematics and statistics applied to polymer chemistry, separation phenomena, and polymer production are the main areas of interest for further research. The trends suggest that Monte Carlo and numerical simulation can contribute to a deeper understanding of PVC's properties and behavior. In addition, the focus on plastics and microplastics reflects concerns about the environmental impact. A bibliometric study evidenced that PSE provides the tools for improvement in PVC production processes by employing advanced process engineering techniques. Modelling and new algorithms for simulation methods of continuous polymerization processes are important to enhance accuracy and efficiency across various applications. The study also proposes a research agenda for future researchers working in the field of the use of PSE applied to the PVC production process.

Application of KNN and ANN Metamodeling for RTM Filling Process Prediction

Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and artificial neural network metamodels is proposed to build predictive surrogate models capable of relating the mold-filling process input-output correlations to assist mold designing. The input features considered are the resin injection location and resin viscosity. The corresponding output features investigated are the number of vents required and the resultant maximum injection pressure. Upon training, both investigated metamodels demonstrated desirable prediction accuracies, with a low prediction error range of 5.0% to 15.7% for KNN metamodels and 6.7% to 17.5% for ANN metamodels. The good prediction results convincingly indicate that metamodeling is a promising option for composite molding applications, with encouraging prospects for data-intensive applications such as process digital twinning.

Economic and Environmental Competitiveness of Ethane-Based Technologies for Vinyl Chloride Synthesis

The synthesis of the vinyl chloride monomer (VCM), employed to manufacture poly(vinyl chloride) (PVC) plastic, primarily relies on oil-derived ethylene, resulting in high costs and carbon footprint. Natural gas-derived ethane in VCM synthesis has long been considered a transformative feedstock to lower emissions and expenses. In this work, we evaluate the environmental potential and economics of recently developed catalytic ethane chlorination technologies for VCM synthesis. We consider the ethylene-based business-as-usual (BAU) route and two different ethane-based processes evaluated at their current development level and their full potential, i.e., ideal conversion and selectivity. All routes are assessed under two temporal scenarios: present (2020) and prospective (2050). Combining process simulation and life cycle assessment (LCA), we find that catalytic ethane chlorination technologies can lower the production cost by 32% at their current development state and by 56% when considering their full potential. Though environmentally disadvantageous in the 2020 scenario, they emerge as more sustainable alternatives to the BAU in the 2050 scenario, reducing the carbon footprint of VCM synthesis by up to 26% at their current state and up to 58% at their full potential. Going beyond VCM synthesis, our results highlight prospective LCA as a powerful tool for assessing the true environmental implications of emerging technologies under more decarbonized future energy scenarios.

Optimization of Components with Topology Optimization for Direct Additive Manufacturing by DLMS

This paper presents a novel design methodology that validates and utilizes the results of topology optimization as the final product shape. The proposed methodology aims to streamline the design process by eliminating the need for remodeling and minimizing printing errors through process simulation. It also eliminates the repeated export and import of data between software tools. The study includes a case study involving the steering column housing of a racing car, where Siemens NX Topology Optimization was used for optimization, and verification analysis was conducted using the NX Nastran solver. The final solution was fabricated using AlSi10Mg via direct metal laser sintering on a 3D printer and successfully validated under real conditions. In conclusion, this paper introduces a comprehensive design methodology for the direct utilization of topology optimization, which was validated through a case study, yielding positive results.

Study of Selective Dry Etching Effects of 15-Cycle Si0.7Ge0.3/Si Multilayer Structure in Gate-All-Around Transistor Process

Gate-all-around (GAA) structures are important for future logic devices and 3D-DRAM. Inner-spacer cavity etching and channel release both require selective etching of Si_0.7Ge_0.3. Increasing the number of channel-stacking layers is an effective way to improve device current-driving capability and storage density. Previous work investigated ICP selective etching of a three-cycle Si_0.7Ge_0.3/Si multilayer structure and the related etching effects. This study focuses on the dry etching of a 15-cycle Si_0.7Ge_0.3/Si multilayer structure and the associated etching effects, using simulation and experimentation. The simulation predicts the random effect of lateral etching depth and the asymmetric effect of silicon nanosheet damage on the edge, both of which are verified by experiments. Furthermore, the study experimentally investigates the influence and mechanism of pressure, power, and other parameters on the etching results. Research on these etching effects and mechanisms will provide important points of reference for the dry selective etching of Si_0.7Ge_0.3 in GAA structures.

[Current research status and application prospect of numerical simulation in traditional Chinese medicine drying]

With the rapid development of computer technology, numerical simulation has gradually become an important method to study drying process and improve drying equipment. Using computer to simulate the drying process of traditional Chinese medicine(TCM) is characterized by intuitiveness, scientificity, and low cost, which serves as an auxiliary means for technical innovation in TCM drying. This paper summarizes the theories of different drying methods and the research status of numerical simulation in drying, introduces the modeling methods and software of numerical simulation, and expounds the significance of numerical simulation modeling in shortening the research and development cycle, improving drying equipment, and optimizing drying parameters. However, the current numerical simulation method for drying process has problems, such as low accuracy, lack of quantitative indicators for the control of simulation results on the process, and insufficient in-depth research on the mechanism of drug quality changes. Furthermore, this paper put forward the application prospect of numerical simulation in TCM drying, providing reference for the further study of numerical simulation in this field.

Effect of Peel Ply on Resin Flow during Vacuum Infusion

Although various simulations have been conducted for the vacuum infusion process, most of the studies have considered only fabrics and flow medium and ignored the effect of peel ply. However, peel ply can affect resin flow because it is placed between the fabrics and flow medium. To verify this, permeability of two types of peel plies was measured, and it was found that the permeability between the peel plies differed significantly. Moreover, the permeability of the peel plies was lower than that of the carbon fabric; thus, peel ply can cause a bottleneck in the flow in the out-of-plane direction. Some 3D flow simulations were conducted in cases of no peel ply and for two types of the peel plies to confirm the effect of peel ply, and experiments were also conducted for two types of the peel plies. It was observed that filling time and flow pattern were highly dependent on the peel plies. The smaller permeability of peel ply has, the greater effect of peel ply is. These results indicate that the permeability of peel ply is one of the dominant factors and should be considered in process design in vacuum infusion. Additionally, by adding one layer of peel ply and applying permeability, the accuracy of flow simulations can be improved for filling time and pattern.

Material Extrusion 3D Printing (ME3DP) Process Simulations of Polymeric Porous Scaffolds for Bone Tissue Engineering

Bone tissue engineering (BTE) is an active area of research for bone defect treatment. Some polymeric materials have recently gained adequate attention as potential materials for BTE applications, as they are biocompatible, biodegradable, inexpensive, lightweight, easy to process, and recyclable. Polyetherimide (PEI), acrylonitrile butadiene styrene (ABS), and polyamide-12 (PA12) are potential biocompatible materials for biomedical applications due to their excellent physical, chemical, and mechanical properties. The current study presents preliminary findings on the process simulations for 3D-printed polymeric porous scaffolds for a material extrusion 3D printing (ME3DP) process to observe the manufacturing constraints and scaffold quality with respect to designed structures (porous scaffolds). Different unit cell designs (ventils, grid, and octet) for porous scaffolds, virtually fabricated using three polymeric materials (PEI, ABS, and PA12), were investigated for process-induced defections and residual stresses. The numerical simulation results concluded that higher dimensional accuracy and control were achieved for grid unit cell scaffolds manufactured using PEI material; however, minimum residual stresses were achieved for grid unit cell scaffolds fabricated using PA12 material. Future studies will include the experimental validation of numerical simulation results and the biomechanical performance of 3D-printed polymeric scaffolds.

Process Simulation and Life Cycle Assessment of Waste Plastics: A Comparison of Pyrolysis and Hydrocracking

Pyrolysis and hydrocracking of plastic waste can produce valuable products with manageable effects on the environment as compared to landfilling and incineration. This research focused on the process simulation and life cycle assessment of the pyrolysis and hydrocracking of high-density polyethylene. Aspen Plus was used as the simulator and the Peng-Robinson thermodynamic model was employed as a fluid package. Additionally, sensitivity analysis was conducted in order to optimize product distribution. Based on the simulation, the hydrocracking process produced value-added fuels, i.e., gasoline and natural gas. In contrast, pyrolysis generated a significant quantity of pyrolysis oil with a high number of cyclo-compounds and char, which are the least important to be utilized as fuels. Moreover, in the later part of the study, life cycle assessment (LCA) was adopted in order to investigate and quantify their impact upon the environment using simulation inventory data, which facilitates finding a sustainable process. Simapro was used as a tool for LCA of the processes and materials used. The results demonstrate that hydrocracking is a better process in terms of environmental impact in 10 out of the 11 impact categories. Overall, the present study proposed a promising comparison based on energy demands, product distribution, and potential environmental impacts, which will help to improve plastic waste management.

Sustainable Process for the Production of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from Renewable Resources: A Simulation Study

Bacterially produced polyhydroxyalkanoates are valuable substitutes for petrochemical plastics, but their current production capacities are very scarce. Producing poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHB-co-HV) from methane and odd-chain carbon fatty acids could make the production of this biodegradable polymer cost-effective. This study analyzes the main factors affecting methanotrophic growth and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) accumulation, simulating a pilot-scale process based on a double-stage approach. The effects of the nitrogen source and the oxygen partial pressure during a 20 day growth phase were studied; the cosubstrate concentration, the culture selected, and the methane partial pressure were investigated during the poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production stage performed within 15 days under nutrient starvation. Methylocystis parvus OBBP and Methylosinus thricosporum OB3b reached the maximum growth productivities with ammonium as the nitrogen source and oxygen at high partial pressure. The simulation of the PHB-co-HV accumulation revealed that methanotrophs could better accumulate the polymer with low valeric acid concentrations. A methane-abundant gas stream (0.5 atm of methane) could increase process yields up to 0.32 kg m^-3 d^-1.

Injection Molding Process Simulation of Polycaprolactone Sticks for Further 3D Printing of Medical Implants

The aim of the present study was a simulation of the injection molding process of polycaprolactone filament sticks for further 3D printing of osteochondral implants. Polycaprolactone data are not available in the data banks of popular injection molding simulation programs. Therefore, thermal and rheological data from the literature were imported to the material database of Solidworks Plastics software to simulate the injection molding process of filament sticks. The influence of several injection molding parameters including melt temperature, injection time, and injection pressure on the geometry of filament stick (final part) was investigated. Based on the results of the performed simulation and analyses, it was possible to improve the injection process parameters. The accuracy of simulation predictions, based on the literature data, demonstrates the potential of using simulation as a tool to develop polycaprolactone parts for future implants and to optimize the injection molding process.

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.

Commissioning Clinical Spaces During a Pandemic: Merging Methodologies of Human Factors and Simulation

OBJECTIVES

The objective of this case study is to demonstrate the value of applying tabletop and simulation techniques to highlight high-risk, high-impact outcomes and organizational recommendations in the commissioning of a new clinical spaces.

PURPOSE/AIM

Generalizability of lessons learned from this case study aim to support other health organizations in commissioning of clinical spaces during communicable disease outbreaks.

BACKGROUND

COVID-19 challenged our healthcare system, requiring teams to prepare in a short span of time. Bridging expertise of human factor and simulation teams provided a novel, interdisciplinary, and timely approach to evaluate and commission spaces.

METHODS

Human factors and simulation teams were enlisted to conduct an evaluation of a new space prior to readiness for delivery of safe patient care. An adapted tabletop evaluation and subsequent systems integration simulation was conducted. The goal of the tabletop exercise was to identify and define processes and risks to tested in the physical space using simulation.

RESULTS

Applying both human factors science and systems simulation proactively identified the highest risk, highest impact outcomes, validated existing processes and allowed for refining of potential solutions and recommendations of the new space. A strong working relationship between teams fostered an opportunity to share information, debrief, evaluate, and adapt methods while applying timely changes based on emergent findings.

CONCLUSIONS

These combined methodologies are important tools that can be learned and applied to healthcare commissioning of new clinical spaces in the identification of high-risk, high-impact outcomes affecting staff and organizational preparedness and safety.

Mental Simulation to Promote Exercise Intentions and Behaviors

Mental simulation, which employs specific patterns of imagery, can increase the intention to exercise as well as actual engagement in exercise. The present studies explored the effects of mental simulation on the intention to engage in exercise while regulating emotions. The first study confirmed that mental simulation did promote intentions of participants. The second found that video-primed mental simulation was a more effective method of exercise intention promotion than semantic-primed and image-primed mental simulation. In the third study, it was found that combining process-based and outcome-based mental simulations increased exercise intentions. Positive emotions mediated imagery ability and intention to exercise. The final study found that the mental simulation interventions most effective for exercise adherence were those that balanced the valence of process and outcome components in such a way that a challenging process results in a positive outcome, or a smooth process results in a negative outcome. Each of these results has practical implications for exercise interventions that will be discussed.

Why Does Not Nanotechnology Go Green? Bioprocess Simulation and Economics for Bacterial-Origin Magnetite Nanoparticles

Nanotechnological developments, including fabrication and use of magnetic nanomaterials, are growing at a fast pace. Magnetic nanoparticles are exciting tools for use in healthcare, biological sensors, and environmental remediation. Due to better control over final-product characteristics and cleaner production, biogenic nanomagnets are preferable over synthetic ones for technological use. In this sense, the technical requirements and economic factors for setting up industrial production of magnetotactic bacteria (MTB)-derived nanomagnets were studied in the present work. Magnetite fabrication costs in a single-stage fed-batch and a semicontinuous process were US$ 10,372 and US$ 11,169 per kilogram, respectively. Depending on the variations of the production process, the minimum selling price for biogenic nanomagnets ranged between US$ 21 and US$ 120 per gram. Because these prices are consistently below commercial values for synthetic nanoparticles, we suggest that microbial production is competitive and constitutes an attractive alternative for a greener manufacturing of magnetic nanoparticles nanotools with versatile applicability.

Thermo-Mechanical Simulation of Underwater Friction Stir Welding of Low Carbon Steel

This article investigates the flow of materials and weld formation during underwater friction stir welding (UFSW) of low carbon steel. A thermo-mechanical model is used to understand the relation between frictional heat phenomena during the welding and weld properties. To better understand the effects of the water environment, the simulation and experimental results were compared with the sample prepared by the traditional friction stir welding (FSW) method. Simulation results from surface heat diffusion indicate a smaller preheated area in front of the FSW tool declined the total generated heat in the UFSWed case compared to the FSWed sample. The simulation results revealed that the strain rate of steel in the stir zone (SZ) of the FSWed joint is higher than in the UFSWed case. The microstructure of the welded sample shows that SZ’s microstructure at the UFSWed case is more refined than the FSWed case due to the higher cooling rate of the water environment. Due to obtained results, the maximum temperatures of FSWed and UFSWed cases were 1228 °C and 1008 °C. Meanwhile, the simulation results show 1200 °C and 970 °C for conventional and underwater FSW samples, respectively. The maximum material velocity in SZ predicted 0.40 m/s and 0.32 m/s for FSW and underwater FSWed samples. The better condition in the UFSW case caused the ultimate tensile strength of welded sample to increase ~20% compared to the FSW joint.

Simulation of Organic Liquid Products Deoxygenation by Multistage Countercurrent Absorber/Stripping Using CO2 as Solvent with Aspen-HYSYS: Thermodynamic Data Basis and EOS Modeling

In this work, the thermodynamic data basis and equation of state (EOS) modeling necessary to simulate the fractionation of organic liquid products (OLP), a liquid reaction product obtained by thermal catalytic cracking of palm oil at 450 °C, 1.0 atmosphere, with 10% (wt.) Na₂CO₃ as catalyst, in multistage countercurrent absorber/stripping columns using supercritical carbon dioxide (SC-CO₂) as solvent, with Aspen-HYSYS was systematically investigated. The chemical composition of OLP was used to predict the density (ρ), boiling temperature (T_b), critical temperature (T_c), critical pressure (P_c), critical volume (V_c), and acentric factor (ω) of all the compounds present in OLP by applying the group contribution methods of Marrero-Gani, Han-Peng, Marrero-Pardillo, Constantinou-Gani, Joback and Reid, and Vetere. The RK-Aspen EOS used as thermodynamic fluid package, applied to correlate the experimental phase equilibrium data of binary systems OLP-_i/CO₂ available in the literature. The group contribution methods selected based on the lowest relative average deviation by computing T_b, T_c, P_c, V_c, and ω. For n-alkanes, the method of Marrero-Gani selected for the prediction of T_c, P_c and V_c, and that of Han-Peng for ω. For alkenes, the method of Marrero-Gani selected for the prediction of T_b and T_c, Marrero-Pardillo for P_c and V_c, and Han-Peng for ω. For unsubstituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of T_b, Marrero-Gani for T_c, Joback for P_c and V_c, and the undirected method of Vetere for ω. For substituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of T_b and P_c, Marrero-Gani for T_c and V_c, and the undirected method of Vetere for ω. For aromatic hydrocarbon, the method of Joback selected for the prediction of T_b, Constantinou-Gani for T_c and V_c, Marrero-Gani for P_c, and the undirected method of Vetere for ω. The regressions show that RK-Aspen EOS was able to describe the experimental phase equilibrium data for all the binary pairs undecane-CO₂, tetradecane-CO₂, pentadecane-CO₂, hexadecane-CO₂, octadecane-CO₂, palmitic acid-CO₂, and oleic acid-CO₂, showing average absolute deviation for the liquid phase (AADx) between 0.8% and 1.25% and average absolute deviation for the gaseous phase (AADy) between 0.01% to 0.66%.

Process simulation and comprehensive evaluation of a system of coal power plant coupled with waste incineration

The technology of coal power plant coupled with waste incineration is considered as a promising technology for fossil fuel conservation and waste disposal. In this paper, a system of coal power plant coupled with waste incineration is simulated by Aspen Plus software, and a conventional coal power plant is also simulated for comparison. Comprehensive evaluation including thermodynamic, economic and environmental impact performances are analysed and compared. Evaluation results indicate that the thermodynamic performance and environmental impact of the system of coal power plant coupled with waste incineration are worse, but the economic performance of the system is obviously better than the coal power plant. When the replacement ratio of waste is 20%, the energy and exergy efficiencies of the system are 38.54% and 37.27%, the internal rate of return and discounted payback period of the system are 21.83% and 9.14 years, and the environmental cost of the system is $3597.73 h^-1. Therefore, the technology of coal power plant coupled with waste incineration has technical feasibility and economic advantages, and the environmental impacts need to be considered in the application of the technology.

Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I₁+I₂)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.

Process Simulation and Environmental Aspects of Dimethyl Ether Production from Digestate-Derived Syngas

The production of dimethyl ether from renewables or waste is a promising strategy to push towards a sustainable energy transition of alternative eco-friendly diesel fuel. In this work, we simulate the synthesis of dimethyl ether from a syngas (a mixture of CO, CO₂ and H₂) produced from gasification of digestate. In particular, a thermodynamic analysis was performed to individuate the best process conditions and syngas conditioning processes to maximize yield to dimethyl etehr (DME). Process simulation was carried out by ChemCAD software, and it was particularly focused on the effect of process conditions of both water gas shift and CO₂ absorption by Selexol^® on the syngas composition, with a direct influence on DME productivity. The final best flowsheet and the best process conditions were evaluated in terms of CO₂ equivalent emissions. Results show direct DME synthesis global yield was higher without the WGS section and with a carbon capture equal to 85%. The final environmental impact was found equal to −113 kgCO₂/GJ, demonstrating that DME synthesis from digestate may be considered as a suitable strategy for carbon dioxide recycling.

New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process

It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO2-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method.

Validation of a Simulation Methodology for Thermoplastic and Thermosetting Composite Materials Considering the Effect of Forming Process on the Structural Performance

This research work investigated the influence of the press molding manufacturing process on the mechanical properties, both for thermoplastic and thermosetting fiber reinforced composite materials. The particular geometry of the case study, called Double Dome, was considered in order to verify the behavior of the Thermoplastic and Thermosetting prepreg in terms of shell thickness variation and fibers shear angle evolution during the thermoforming process. The thermoforming simulation was performed using LS-DYNA^® Finite Element Analysis (FEA) code, and the results were transferred by Envyo^®, a dedicated mapping tool, into a LS-DYNA^® virtual model for the structural simulation. A series of Double Dome specimens was produced with industrial equipment, and a bending experimental test was been carried on. Finally, a numerical-experimental correlation was performed, highlighting a significant forecast of the mechanical properties for the considered component.

Integrating Pressure-Driven Membrane Separation Processes to Improve Eco-Efficiency in Cheese Manufacture: A Preliminary Case Study

Pressure-driven membrane separation processes are commonly used in cheese milk standardization. Using ultrafiltration (UF) or microfiltration (MF), membrane separation processes make it possible to concentrate the milk proteins and increase the yields of cheese vats. However, the contribution of membrane separation processes to the environmental impact and economical profitability of dairy processes is still unclear. The objective of this study was to evaluate the contribution of membrane separation processes to the eco-efficiency of cheddar cheese production in Québec (Canada) using process simulation. Three scenarios were compared: two included UF or MF at the cheese milk standardization step, and one did not incorporate membrane separation processes. The results showed that even if membrane separation processes make it possible to increase vat yields, they do not improve the eco-efficiency of cheddar cheese processes. However, membrane separation processes may benefit the eco-efficiency of the process more when used for byproduct valorization.

[Accounting Methods of VOCs Emission Associated with Production Processes in a Fine Chemical Industrial Park]

Volatile organic compounds (VOCs) are one of the key atmospheric pollutants associated with great impact on air quality. Industrial sources have become the most important source of VOCs emissions in China, of which the chemical industry accounts for a large part. At present, more than half of chemical manufacturers are clustered in chemical industrial parks (CIPs), thus the control of VOCs in CIPs is crucial. This study analyzed the production processes of the fine chemical industry and the principle of VOCs production, and then proposed an accounting model of VOCs emissions associated with production processes, based on a typical fine chemical industrial park:Hangzhou Bay Shangyu Economic and Technological Development Area (HSEDA). The quantity of VOCs generation in the main production processes, such as feeding, heating, chemical reaction gas take-off, cleaning, vacuum pumping, pressure release, and evaporating, were calculated. Meanwhile, the chemical process simulation software Aspen was also employed to simulate the VOCs generation in these processes. The methods were applied to 14 representative products in HSEDA and the feature of VOCs generation in the chemical processes mentioned above, and components were revealed. The results of the two methods were compared, and the difference between the two methods was within±22% except for the pressure release process. The model and accounting methods proposed in this study will have sound applicability in accounting for VOCs emissions in fine chemical industrial parks underpinned by quantitative chemical industry production parameters.

Techno-Economic Assessment of Bio-Syngas Production for Methanol Synthesis: A Focus on the Water-Gas Shift and Carbon Capture Sections

The biomass-to-methanol process may play an important role in introducing renewables in the industry chain for chemical and fuel production. Gasification is a thermochemical process to produce syngas from biomass, but additional steps are requested to obtain a syngas composition suitable for methanol synthesis. The aim of this work is to perform a computer-aided process simulation to produce methanol starting from a syngas produced by oxygen-steam biomass gasification, whose details are reported in the literature. Syngas from biomass gasification was compressed to 80 bar, which may be considered an optimal pressure for methanol synthesis. The simulation was mainly focused on the water-gas shift/carbon capture sections requested to obtain a syngas with a (H₂ - CO₂)/(CO + CO₂) molar ratio of about 2, which is optimal for methanol synthesis. Both capital and operating costs were calculated as a function of the CO conversion in the water-gas shift (WGS) step and CO₂ absorption level in the carbon capture (CC) unit (by Selexol^® process). The obtained results show the optimal CO conversion is 40% with CO₂ capture from the syngas equal to 95%. The effect of the WGS conversion level on methanol production cost was also assessed. For the optimal case, a methanol production cost equal to 0.540 €/kg was calculated.

Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling

The increasing demand for micro-injection molding process technology and the corresponding micro-molded products have materialized in the need for models and simulation capabilities for the establishment of a digital twin of the manufacturing process. The opportunities enabled by the correct process simulation include the possibility of forecasting the part quality and finding optimal process conditions for a given product. The present work displays further use of micro-injection molding process simulation for the prediction of feature dimensions and its optimization and microfeature replication behavior due to geometrical boundary effects. The current work focused on the micro-injection molding of three-dimensional microparts and of single components featuring microstructures. First, two virtual a studies were performed to predict the outer diameter of a micro-ring within an accuracy of 10 µm and the flash formation on a micro-component with mass a 0.1 mg. In the second part of the study, the influence of microstructure orientation on the filling time of a microcavity design section was investigated for a component featuring micro grooves with a 15 µm nominal height. Multiscale meshing was employed to model the replication of microfeatures in a range of 17–346 µm in a Fresnel lens product, allowing the prediction of the replication behavior of a microfeature at 91% accuracy. The simulations were performed using 3D modeling and generalized Navier–Stokes equations using a single multi-scale simulation approach. The current work shows the current potential and limitations in the use of micro-injection molding process simulations for the optimization of micro 3D-part and microstructured components.

4D Micro-Computed X-ray Tomography as a Tool to Determine Critical Process and Product Information of Spin Freeze-Dried Unit Doses

Maintaining chemical and physical stability of the product during freeze-drying is important but challenging. In addition, freeze-drying is typically associated with long process times. Therefore, mechanistic models have been developed to maximize drying efficiency without altering the chemical or physical stability of the product. Dried product mass transfer resistance ( R p ) is a critical input for these mechanistic models. Currently available techniques to determine R p only provide an estimation of the mean R p and do not allow measuring and determining essential local (i.e., intra-vial) R p differences. In this study, we present an analytical method, based on four-dimensional micro-computed tomography (4D- μ CT), which enables the possibility to determine intra-vial R p differences. Subsequently, these obtained R p values are used in a mechanistic model to predict the drying time distribution of a spin-frozen vial. Finally, this predicted primary drying time distribution is experimentally verified via thermal imaging during drying. It was further found during this study that 4D- μ CT uniquely allows measuring and determining other essential freeze-drying process parameters such as the moving direction(s) of the sublimation front and frozen product layer thickness, which allows gaining accurate process knowledge. To conclude, the study reveals that the variation in the end of primary drying time of a single vial could be predicted accurately using 4D- μ CT as similar results were found during the verification using thermal imaging.

Advances in Polyhydroxyalkanoate (PHA) Production, Volume 2

During the two years that have passed since the first volume of “Advances in Polyhydroxyalkanoate (PHA) production” was published, the progress in PHA-related research was indeed tremendous, calling for the next, highly bioprocess- and bioengineering-oriented volume. This editorial paper summarizes and puts into context the contributions to this second volume of the Bioengineering Special Issue; it covers highly topical fields of PHA-related R&D activities, covering, beside the pronounced bioengineering-related articles, the fields of the microbiology of underexplored, but probably emerging, PHA production strains from the groups of Pseudomonas, cyanobacteria, methanotrophs, and from the extremophilic domain of haloarchaea. Moreover, novel second-generation lignocellulose feedstocks for PHA production from agriculture to be used in biorefinery concepts, new approaches for fine-tuning the composition of PHA co- and terpolyesters, process simulation for PHA production from methane-rich natural gas, the challenges associated with rheology-governed oxygen transfer in high cell density cultivations, rapid spectroscopic in-line analytics for process monitoring, and the biomedical application of PHA biopolyesters after appropriate advanced processing are the subjects of the presented studies.

Consolidation Modeling during Thermoforming of Thermoplastic Composite Prepregs

This article describes the modeling of the compaction/consolidation behavior of thermoplastic composite prepregs during the thermoforming process. The proposed model is principally based on a generalized Maxwell approach. Within a hyperelastic framework, viscoelasticity is introduced for the compaction mode in addition to the in-plane shearing mode by taking into account the influence of the resin and its flow during consolidation. To reveal the evolution of the consolidation level, which reflects the number of voids in the composite, an intimate contact model was used during the process. The model was characterized by a compaction test at a high temperature. It was implemented into a recently developed prismatic solid-shell finite element. The analysis of the thermoforming of a double dome demonstrated the relevance of the consolidation computation in determining the process parameters leading to a composite part free of voids.

On the Use of Ultrafiltration or Microfiltration Polymeric Spiral-Wound Membranes for Cheesemilk Standardization: Impact on Process Efficiency

Ultrafiltration (UF) and microfiltration (MF) are widely-used technologies to standardize the protein content of cheesemilk. Our previous work demonstrated that protein retention of a 0.1-µm MF spiral-wound membrane (SWM) was lower, but close to that of a 10 kDa UF one. Considering that the permeability of MF membranes is expected to be higher than that of UF ones, it was hypothesized that the former could improve the efficiency of the cheesemaking process. Consequently, the objectives of this work were to compare 0.1-µm MF and 10 kDa UF spiral-wound membranes in terms of (1) hydraulic and separation performance, (2) energy consumption and fouling behavior, (3) cheesemaking efficiency of retentates enriched with cream, and (4) economic performance in virtual cheesemaking plants. This study confirmed the benefits of using MF spiral-wound membranes to reduce the specific energy consumption of the filtration process (lower hydraulic resistance and higher membrane permeability) and to enhance the technological performance of the cheesemaking process (higher vat yield, and protein and fat recoveries). However, considering the higher serum protein retention of the UF membrane and the low price of electricity in Canada, the UF scenario remained more profitable. It only becomes more efficient to substitute the 10 kDa UF SWM by the 0.1-μm MF when energy costs are substantially higher.

Scale-Up Strategy in Quality by Design Approach for Pharmaceutical Blending Process with Discrete Element Method Simulation

An approach combining quality by design (QbD) and the discrete element method (DEM) is proposed to establish an effective scale-up strategy for the blending process of an amlodipine formulation prepared by the direct compression method. Critical process parameters (CPPs) for intermediate critical quality attributes (IQAs) were identified using risk assessment (RA) in the QbD approach. A Box–Behnken design was applied to obtain the operating space for a laboratory-scale. A DEM model was developed by the input parameters for the amlodipine formulation; blending was simulated on a laboratory-scale V-blender (3 L) at optimal settings. The efficacy and reliability of the DEM model was validated through a comparison of simulation and experimental results. Change of operating space was evaluated using the validated DEM model when scaled-up to pilot-scale (10 L). Pilot-scale blending was simulated on a V-blender and double-cone blender at the optimal settings derived from the laboratory-scale operating space. Both pilot-scale simulation results suggest that blending time should be lower than the laboratory-scale optimized blending time to meet target values. These results confirm the change of operating space during the scale-up process. Therefore, this study suggests that a QbD-integrated DEM simulation can be a desirable approach for an effective scale-up strategy.

A Semi-Analytical Model to Predict Infusion Time and Reinforcement Thickness in VARTM and SCRIMP Processes

In liquid composite molding processes, such as resin transfer molding (RTM) and vacuum assisted resin transfer molding (VARTM), the resin is drawn through fiber preforms in a closed mold by an induced pressure gradient. Unlike the RTM, where a rigid mold is employed, in VARTM, a flexible bag is commonly used as the upper-half mold. In this case, fabric deformation can take place during the impregnation process as the resin pressure inside the preform changes, resulting in continuous variations of reinforcement thickness, porosity, and permeability. The proper approach to simulate the resin flow, therefore, requires coupling deformation and pressure field making the process modeling more complex and computationally demanding. The present work proposes an efficient methodology to add the effects of the preform compaction on the resin flow when a deformable porous media is considered. The developed methodology was also applied in the case of Seeman's Composite Resin Infusion Molding Process (SCRIMP). Numerical outcomes highlighted that preform compaction significantly affects the resin flow and the filling time. In particular, the more compliant the preform, the more time is required to complete the impregnation. On the other hand, in the case of SCRIMP, the results pointed out that the resin flow is mainly ruled by the high permeability network.

Process Variability-Technological Challenge and Design Issue for Nanoscale Devices

Current advanced transistor architectures, such as FinFETs and (stacked) nanowires and nanosheets, employ truly three-dimensional architectures. Already for aggressively scaled bulk transistors, both statistical and systematic process variations have critically influenced device and circuit performance. Three-dimensional device architectures make the control and optimization of the device geometries even more important, both in view of the nominal electrical performance to be achieved and its variations. In turn, it is essential to accurately simulate the device geometry and its impact on the device properties, including the effect caused by non-idealized processes which are subject to various kinds of systematic variations induced by process equipment. In this paper, the hierarchical simulation system developed in the SUPERAID7 project to study the impact of variations from equipment to circuit level is presented. The software system consists of a combination of existing commercial and newly developed tools. As the paper focuses on technological challenges, especially issues resulting from the structuring processes needed to generate the three-dimensional device architectures are discussed. The feasibility of a full simulation of the impact of relevant systematic and stochastic variations on advanced devices and circuits is demonstrated.

Process Simulation and Cost Evaluation of Carbon Membranes for CO₂ Removal from High-Pressure Natural Gas

Natural gas sweetening is required to remove the acid gas CO₂ to meet gas grid specifications. Membrane technology has a great potential in this application compared to the state-of-the-art amine absorption technology. Carbon membranes are of particular interest due to their high CO₂/CH₄ selectivity of over 100. In order to document the advantages of carbon membranes for natural gas (NG) sweetening, HYSYS simulation and cost evaluation were conducted in this work. A two-stage carbon membrane process with recycling in the second stage was found to be technically feasible to achieve >98% CH₄ with <2% CH₄ loss. The specific natural gas processing cost of 1.122 × 10⁻² $/m³ sweet NG was estimated at a feed pressure of 90 bar, which was significantly dependent on the capital-related cost. Future work on improving carbon membrane performance is required to increase the competitiveness of carbon membranes for natural gas sweetening.

Accelerating Flux Calculations Using Sparse Sampling

The ongoing miniaturization in electronics poses various challenges in the designing of modern devices and also in the development and optimization of the corresponding fabrication processes. Computer simulations offer a cost- and time-saving possibility to investigate and optimize these fabrication processes. However, modern device designs require complex three-dimensional shapes, which significantly increases the computational complexity. For instance, in high-resolution topography simulations of etching and deposition, the evaluation of the particle flux on the substrate surface has to be re-evaluated in each timestep. This re-evaluation dominates the overall runtime of a simulation. To overcome this bottleneck, we introduce a method to enhance the performance of the re-evaluation step by calculating the particle flux only on a subset of the surface elements. This subset is selected using an advanced multi-material iterative partitioning scheme, taking local flux differences as well as geometrical variations into account. We show the applicability of our approach using an etching simulation of a dielectric layer embedded in a multi-material stack. We obtain speedups ranging from 1.8 to 8.0, with surface deviations being below two grid cells (0.6–3% of the size of the etched feature) for all tested configurations, both underlining the feasibility of our approach.

[Simulation of Nitrate Isotopic (δ15N and δ18O) by Coupling the Hydrology and Transport Processes Described by the SWAT Model]

To improve the reliability of methods to trace surface water pollutants in river basins, hydrological and water quality processes in the Fuxi River Basin were continuously monitored from 2013 to 2015, and the main pollution sources in the watershed and δ¹⁵N as well as δ¹⁸O in the rivers were measured simultaneously. The Soil and Water Assessment Tool (SWAT) model was used to simulate the NH₄⁺ and NO₃^- migration processes in the hydrological processes of the land surface and rivers. On this basis, the processes of mixing, transformation, and fractionation of δ¹⁵N and δ¹⁸O in NO₃^- were coupled, and the simulation methods of δ¹⁵N and δ¹⁸O in the rivers were developed. The results showed that δ¹⁵N and δ¹⁸O in the rivers were mainly affected by the pollution sources in the river basin and the variation in runoff conditions during different hydrological periods. The contribution of the mixing process of different isotopes to the isotope abundance was 82.74%. The contribution of isotope fractionation in the process of nitrogen conversion was 16.26%. The influence of NH₄⁺ and NO₃^- concentration deviation from the SWAT simulation on the simulation errors of δ¹⁵N and δ¹⁸O was 10.44%. The δ¹⁸O simulation errors were 18.72% larger than those of δ¹⁵N because of the higher variation range of δ¹⁸O in rainfall and the complexity of δ¹⁸O. The systematic errors and deviations of the simulated δ¹⁵N and δ¹⁸O results using the proposed method were less than 10% and 15%, respectively. The simulation method of δ¹⁵N and δ¹⁸O in the river basin has a clear physical meaning, which provides a useful approach for tracing nitrogen sources in rivers.

Data Quality and Assessment, Validation Methods and Error Propagation through the Simulation Software: Report from the Round-Table Discussion at the 10th World Congress of Chemical Engineering in Barcelona (October 1-5, 2017)

The issues of data quality and propagation of data uncertainties into process design and plant specifications are of great current interest. Hence, two Working Parties of the European Federation of Chemical Engineers (EFCE) organized a Round Table Discussion on the topic, as part of the World Congress of Chemical Engineering (WCCE10) in Barcelona, in October 2017. The discussion was guided by industrial and academic experts, with the audience as a key part of the discussion, trying to find some answers in three areas: Data acquisition and evaluation of experimental uncertainties, tools for data reconciliation to improve their quality, and impact of data uncertainties on the process at the end. Several concrete stories are presented that demonstrate the importance of considering data quality and all possible contributions to the uncertainty of chemical process design. Difficulties associated with data quality are discussed at various levels: (1) the experimentalists (measurement issues, evaluation of uncertainties, use of consistency analysis tools); (2) model developers (capture of adequate physics, parameter regression strategies, uncertainty propagation), (3) vendors of process simulation software, and (4) process engineers (who are responsible at the end). Paths for improvements were proposed through better and more efficient communication among different participants, as well as through education.

Multiscale characterization and representation of composite materials during processing

Given the importance of residual stresses and dimensional changes in composites manufacturing, process simulation has been the focus of many studies in recent years. Consequently, various constitutive models and simulation approaches have been developed and implemented for composites process simulation. In this paper, various constitutive models, ranging from elastic to nonlinear viscoelastic; and simulation approaches ranging from separated flow/solid phases to multiscale integrated phases are presented and their applicability for process simulation is discussed. Attention has been paid to practical aspects of the problem where the complexity of the model coupled with the complexity and size scaling of the structure increases the characterization and simulation costs. Two specific approaches and their application are presented in detail: the pseudo-viscoelastic cure hardening instantaneously linear elastic (CHILE) and linear viscoelastic (VE). It is shown that CHILE can predict the residual stress formation in simple cure cycles such as the one-hold cycle for HEXCEL AS4/8552 where the material does not devitrify during processing. It is also shown that using this simple approach, the cure cycle can be modified to lower the residual stress level and therefore increase the mechanical performance of the composite laminate. For a more complex cure cycle where the material is devitrified during a post-cure, it is shown that a more complex model such as VE is required. This article is part of the themed issue 'Multiscale modelling of the structural integrity of composite materials'.

Virtual milk for modelling and simulation of dairy processes

The modeling of dairy processing using a generic process simulator suffers from shortcomings, given that many simulators do not contain milk components in their component libraries. Recently, pseudo-milk components for a commercial process simulator were proposed for simulation and the current work extends this pseudo-milk concept by studying the effect of both total milk solids and temperature on key physical properties such as thermal conductivity, density, viscosity, and heat capacity. This paper also uses expanded fluid and power law models to predict milk viscosity over the temperature range from 4 to 75°C and develops a succinct regressed model for heat capacity as a function of temperature and fat composition. The pseudo-milk was validated by comparing the simulated and actual values of the physical properties of milk. The milk thermal conductivity, density, viscosity, and heat capacity showed differences of less than 2, 4, 3, and 1.5%, respectively, between the simulated results and actual values. This work extends the capabilities of the previously proposed pseudo-milk and of a process simulator to model dairy processes, processing different types of milk (e.g., whole milk, skim milk, and concentrated milk) with different intrinsic compositions, and to predict correct material and energy balances for dairy processes.

Simulation modeling of high-throughput cryopreservation of aquatic germplasm: a case study of blue catfish sperm processing

Emerging commercial-level technology for aquatic sperm cryopreservation has not been modeled by computer simulation. Commercially available software (ARENA, Rockwell Automation, Inc. Milwaukee, WI) was applied to simulate high-throughput sperm cryopreservation of blue catfish (Ictalurus furcatus) based on existing processing capabilities. The goal was to develop a simulation model suitable for production planning and decision making. The objectives were to: 1) predict the maximum output for 8-hr workday; 2) analyze the bottlenecks within the process, and 3) estimate operational costs when run for daily maximum output. High-throughput cryopreservation was divided into six major steps modeled with time, resources and logic structures. The modeled production processed 18 fish and produced 1164 ± 33 (mean ± SD) 0.5-ml straws containing one billion cryopreserved sperm. Two such production lines could support all hybrid catfish production in the US and 15 such lines could support the entire channel catfish industry if it were to adopt artificial spawning techniques. Evaluations were made to improve efficiency, such as increasing scale, optimizing resources, and eliminating underutilized equipment. This model can serve as a template for other aquatic species and assist decision making in industrial application of aquatic germplasm in aquaculture, stock enhancement, conservation, and biomedical model fishes.

Effects of production and market factors on ethanol profitability for an integrated first and second generation ethanol plant using the whole sugarcane as feedstock

BACKGROUND

Sugarcane is an attractive feedstock for ethanol production, especially if the lignocellulosic fraction can also be treated in second generation (2G) ethanol plants. However, the profitability of 2G ethanol is affected by the processing conditions, operating costs and market prices. This study focuses on the minimum ethanol selling price (MESP) and maximum profitability of ethanol production in an integrated first and second generation (1G + 2G) sugarcane-to-ethanol plant. The feedstock used was sugarcane juice, bagasse and leaves. The lignocellulosic fraction was hydrolysed with enzymes. Yields were assumed to be 95% of the theoretical for each of the critical steps in the process (steam pretreatment, enzymatic hydrolysis (EH), fermentation, solid/liquid separation, anaerobic digestion) in order to obtain the best conditions possible for ethanol production, to assess the lowest production costs. Techno-economic analysis was performed for various combinations of process options (for example use of pentoses, addition of leaves), EH conditions (water-insoluble solids (WIS) and residence time), operating cost (enzymes) and market factors (wholesale prices of electricity and ethanol, cost of the feedstock).

RESULTS

The greatest reduction in 2G MESP was achieved when using the pentoses for the production of ethanol rather than biogas. This was followed, in decreasing order, by higher enzymatic hydrolysis efficiency (EHE), by increasing the WIS to 30% and by a short residence time (48 hours) in the EH. The addition of leaves was found to have a slightly negative impact on 1G + 2G MESP, but the effect on 2G MESP was negligible. Sugarcane price significantly affected 1G + 2G MESP, while the price of leaves had a much lower impact. Net present value (NPV) analysis of the most interesting case showed that integrated 1G + 2G ethanol production including leaves could be more profitable than 1G ethanol, despite the fact that the MESP was higher than in 1G ethanol production.

CONCLUSIONS

A combined 1G + 2G ethanol plant could potentially outperform a 1G plant in terms of NPV, depending on market wholesale prices of ethanol and electricity. Therefore, although it is more expensive than 1G ethanol production, 2G ethanol production can make the integrated 1G + 2G process more profitable.

Yeast biomass production: a new approach in glucose-limited feeding strategy

The aim of this work was to implement experimentally a simple glucose-limited feeding strategy for yeast biomass production in a bubble column reactor based on a spreadsheet simulator suitable for industrial application. In biomass production process using Saccharomyces cerevisiae strains, one of the constraints is the strong tendency of these species to metabolize sugars anaerobically due to catabolite repression, leading to low values of biomass yield on substrate. The usual strategy to control this metabolic tendency is the use of a fed-batch process in which where the sugar source is fed incrementally and total sugar concentration in broth is maintained below a determined value. The simulator presented in this work was developed to control molasses feeding on the basis of a simple theoretical model in which has taken into account the nutritional growth needs of yeast cell and two input data: the theoretical specific growth rate and initial cell biomass. In experimental assay, a commercial baker’s yeast strain and molasses as sugar source were used. Experimental results showed an overall biomass yield on substrate of 0.33, a biomass increase of 6.4 fold and a specific growth rate of 0.165 h⁻¹ in contrast to the predicted value of 0.180 h-1 in the second stage simulation.

Bioelectricity versus bioethanol from sugarcane bagasse: is it worth being flexible?

BACKGROUND

Sugarcane is the most efficient crop for production of (1G) ethanol. Additionally, sugarcane bagasse can be used to produce (2G) ethanol. However, the manufacture of 2G ethanol in large scale is not a consolidated process yet. Thus, a detailed economic analysis, based on consistent simulations of the process, is worthwhile. Moreover, both ethanol and electric energy markets have been extremely volatile in Brazil, which suggests that a flexible biorefinery, able to switch between 2G ethanol and electric energy production, could be an option to absorb fluctuations in relative prices. Simulations of three cases were run using the software EMSO: production of 1G ethanol + electric energy, of 1G + 2G ethanol and a flexible biorefinery. Bagasse for 2G ethanol was pretreated with a weak acid solution, followed by enzymatic hydrolysis, while 50% of sugarcane trash (mostly leaves) was used as surplus fuel.

RESULTS

With maximum diversion of bagasse to 2G ethanol (74% of the total), an increase of 25.8% in ethanol production (reaching 115.2 L/tonne of sugarcane) was achieved. An increase of 21.1% in the current ethanol price would be enough to make all three biorefineries economically viable (11.5% for the 1G + 2G dedicated biorefinery). For 2012 prices, the flexible biorefinery presented a lower Internal Rate of Return (IRR) than the 1G + 2G dedicated biorefinery. The impact of electric energy prices (auction and spot market) and of enzyme costs on the IRR was not as significant as it would be expected.

CONCLUSIONS

For current market prices in Brazil, not even production of 1G bioethanol is economically feasible. However, the 1G + 2G dedicated biorefinery is closer to feasibility than the conventional 1G + electric energy industrial plant. Besides, the IRR of the 1G + 2G biorefinery is more sensitive with respect to the price of ethanol, and an increase of 11.5% in this value would be enough to achieve feasibility. The ability of the flexible biorefinery to take advantage of seasonal fluctuations does not make up for its higher investment cost, in the present scenario.