| 1. |
- Ahlström, Johan, 1990, et al.
(författare)
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Forest residues gasification integrated with electrolysis for production of SNG – modelling and assessment
- 2018
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; 44, s. 109-114
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This study investigates opportunities for integrating an electrolysis unit with a biomass gasifier for production of synthetic natural gas (SNG). Gasification is a key technology for production of biofuels and chemicals from lignocellulosic biomass, for which an increased demand is expected in the future. H2produced through an electrolyser can be used to increase the output of a gasifier by reaction with CO2to form CH4. Four integrated flowsheet configurations are evaluated with respect to system energy efficiency and process operating revenue. The system energy efficiencies are in the range of 0.55 – 0.8, and the maximum value of operating revenues is 0.245 $/kWhdry biomass. The results show that feeding the Sabatier reactor with the full product gas flow coming from the methanation unit, and separating the unreacted CO2afterwards, is the most attractive configuration with respect to operating revenue.
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| 2. |
- Andersson, Niklas, et al.
(författare)
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Calibration of a polyethylene plant for grade change optimisations
- 2011
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Ingår i: 21ST European Symposium on Computer Aided Process Engineering. - 1570-7946. - 9780444538956 ; 29, s. 673-677
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Konferensbidrag (refereegranskat)abstract
- A polyethylene plant model coded in Modelica and based on a nonlinear MPC model currently used at Borealis AB is considered for calibration. A case study of model calibration at steady-state for four different operating points are analysed, both when looking at one operating point separately, but also to calibrate several simultaneously. Both model parameters and reactor inputs are calibrated for true plant measurement data. To solve the parameter estimation problem, the JModelica.org platform is used, offering tools to express and solve calibration problems. Calibration was obtained with narrow confidence intervals and shows a potential to improve the model accuracy by changing the parameter values. The results will be used for dynamic optimisations of grade changes.
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| 3. |
- Badr, Sara, 1985, et al.
(författare)
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Combined basic and fine chemical biorefinery concepts with integration of processes at different technology readiness levels
- 2018
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; 43, s. 1577-1582
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Biorefineries offer promising alternatives to the use of fossil fuels to produce energy and chemicals. This work describes the development of a biorefinery concept to produce adipic acid from Swedish forest residues and lutein from micro-algae. A description is provided for each process line available, its technology readiness level (TRL) and the tools available to model it. Scenarios of the integrated concept are built with associated material flow analysis. Key results of the material inventory of the base case scenario are presented as well as insights into the development of further scenarios. Material flow inventories can then be further used for economic and environmental assessment. Major challenges of integration are discussed in terms of uncertainties and data gaps for processes with low TRL such as scaling up lab experiments, understanding the restrictions of material recycling and its impact on process performance. The feedback given through these scenarios can help guide further experimental efforts.
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| 4. |
- Caccavo, D., et al.
(författare)
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Modeling the mechanics and the transport phenomena in hydrogels
- 2018
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; 42, s. 357-383
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Hydrogels are polymeric materials widely used in pharmaceutical and biomedical applications. Their uses can be improved by modeling their behavior, in particular the mechanical phenomena and the transport phenomena. The scope of this chapter is to propose a model, simple enough and with a limited number of parameters to be determined, able to capture the full behavior of a swelling hydrogel, with the aim of describing the drug release process as well as—in principle—any other application of hydrogels. The model was derived recalling the basics of the continuum mechanics, the possible approaches to estimate the Helmholtz free energy, and then writing the transport and constitutive equations for a poroelastic material, and for a more realistic poroviscoelastic material (by adding the standard linear solid model as the rheological model). A full extension to multicomponent systems, to describe the drug release phenomenon, is proposed along with a sensitivity analysis (free-swelling simulation by changing the model parameters).
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| 5. |
- Calvo-Serrano, Raul, et al.
(författare)
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Cradle-to-gate environmental impact prediction from chemical attributes using mixed-integer programming
- 2017
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; 40, s. 1999-2004
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Life Cycle Assessment (LCA) has recently gained widespread acceptance in green chemistry as an effective tool for quantifying the environmental impact of chemicals along their life cycle. Unfortunately, LCA studies require large amounts of data that are hard to gather in practice, a limitation that is particularly critical when assessing the complex processes and value chains present in the chemical industry. With the aim at simplifying these calculations and promoting the wider adoption of environmental principles, in this work we develop an approach that predicts the cradle-to-gate life cycle production impact of organic chemicals from attributes based on their molecular structure and thermodynamic properties. The approach presented relies on a mixed-integer programming (MIP) optimisation framework that streamlines the LCA calculations by systematically constructing multi-linear short-cut predictive models of cradle-to-gate life cycle impact. These models contain key molecular and thermodynamic attributes that are identified using binary variables. On applying our method to an LCA data set containing 83 chemicals, 17 molecular descriptors and 15 thermodynamic properties, we produced estimates for widely used metrics such as cumulative energy demand (CED), global warming potential (GWP) and Eco-indicator 99 (EI99) with relative errors within acceptable ranges considering the nature of any LCA study. Our optimisation-based streamlined LCA framework ultimately leads to simple linear models that are amenable for implementation in computer aided molecular design software, thereby opening new avenues for the inclusion of sustainability principles in the early stages of the development of new chemicals.
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| 6. |
- Chen, Hao, et al.
(författare)
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Dynamic Modelling and Surrogate-based Optimization of Auto-thermal Reforming for Enhanced Hydrogen Production
- 2024
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Ingår i: Computer Aided Chemical Engineering. - : Elsevier B.V.. - 1570-7946. ; 53, s. 1027-1032
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Tidskriftsartikel (refereegranskat)abstract
- Hydrogen energy has been considered as one of the solutions to achieve the net-zero emission scenario by 2050. Steam methane reforming is a widely used industrial process for producing hydrogen from natural gas or methane nowadays. Considering that methane could be utilized as a suitable carrier for hydrogen energy, it is anticipated that steam methane reforming will still play an important role in the future energy sector when it comes to hydrogen production, storage, and transportation. In this work, a one-imensional dynamic model is established to simulate the performance of an auto-thermal reforming reactor, which allows for capturing the localized phenomena inside the reactor over time. A set of input parameters is selected based on the Latin Hypercube Sampling method to generate the training data for the surrogate model development. Singular value decomposition and Gaussian Process regression are then implemented on the training data to construct a surrogate model of the reformer. This surrogate model is subsequently utilized in the optimization process to enhance hydrogen production and lower the maximum catalyst temperature within the reactor. The results show that the surrogate model, developed by using singular value decomposition and Gaussian Process, exhibits a high level of accuracy when compared to the physics-based reformer model. Furthermore, the optimization framework built upon surrogate modelling offers the potential to substantially reduce the computational expenses associated with the optimization process, while preserving the precision of the optimization results. This method could efficiently serve as a tool for parameters optimization of such reactors and could be used to guide the operation of these systems toward improved performance.
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| 7. |
- Holmqvist, Anders, et al.
(författare)
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A Generic PAT Software Interface for On-Line Monitoring and Control of Chromatographic Separation Systems
- 2016
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Ingår i: 26th European Symposium on Computer Aided Process Engineering. - 1570-7946. - 9780444634283 ; 38, s. 811-816
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Konferensbidrag (refereegranskat)abstract
- This contribution presents a novel process analytical technology (PAT) software interface for online monitoring and control of commercial high-pressure liquid chromatography (HPLC) systems. The developed interface is an add-on to chromatography control software and uses industry-standard bidirectional communication protocols to link sensor technologies with the individual HPLC system components in an overall automation framework that facilitates data acquisition, central operation and control of all instruments. The interface is encoded in the Python™ scripting language and supports versatile data transfer to chromatography control software using either OPC (OLE for process control) or COM (component object model) technologies, which are both based on client/server architectures. By these means, the interface utilizes the flexibility of the high-level programming language for formulating optimal control strategies and enables (semantic) interoperability between the chromatography control software and user defined scripts as well as third-party scientific libraries and numerical packages. The advantages and applicability of the developed interface are highlighted through the implementation of a model-based iterative learning control strategy, in order to assure batch-to-batch repeatability, and open-loop optimal controlled elution trajectories on a commercial HPLC separation system. It is, however, noteworthy that the software interface is completely generic and constitutes a novel framework for implementing any PID control schemes as well as sequential optimal experimental design and model predictive control strategies.
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| 8. |
- Holmqvist, Anders, et al.
(författare)
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Dynamic Multi-Objective Optimization of Batch Chromatographic Separation Processes
- 2015
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. - 9780444634290 ; 37, s. 815-820
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Konferensbidrag (refereegranskat)abstract
- This contribution presents a novel offline dynamic multi-objective optimization framework for high-pressure liquid chromatographic (HPLC) processes in batch elution mode. The framework allows for optimization of general elution trajectories parametrized with piecewise constant control signals. It is based on a simultaneous method where both the control and state variables are fully discretized in the temporal domain, using orthogonal collocations on finite elements, and the state variables are discretized in the spatial domain, using a finite volume weighted essentially non-oscillatory (WENO) scheme. The resulting finite dimensional nonlinear program (NLP) is solved using a primal-dual interior point method and automatic differentiation techniques. The advantages of this open-loop optimal control methodology are highlighted through the solution of a challenging ternary complex mixture separation problem for a hydrophobic interaction chromatography (HIC) system. For a bi-objective optimization of the target component productivity and yield, subject to a purity constraint, the set of Pareto solutions generated with general elution trajectories showed considerable improvement in the productivity objective when compared to the Pareto set obtained using conventional linear elution trajectories.
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| 9. |
- Karka, P., et al.
(författare)
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Life Cycle Assessment of Biorefinery Products Based on Different Allocation Approaches
- 2015
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; 37, s. 2573-2578
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Biorefineries constitute representative examples of multifunctional systems which are able to produce, similarly to conventional petroleum refineries, a wide range of chemicals (pharmaceutical constituents, plastics, food additives etc.), energy carriers and power through the optimal use of diverse biomass forms (wheat straw, oils, wood chips, municipal solid waste). For this purpose, biorefineries typically comprise a complicated, integrated network of physical and chemical transformation processes, such as mechanical and physical biomass pretreatment, pyrolysis, gasification, catalytic and enzymatic reactions, and downstream purification processes. For the environmental sustainability assessment of these complicated production systems, Life Cycle Analysis (LCA, ISO-Norm 14040) is considered as a widely acceptable methodology from scientists and engineers including, however, the debated aspect of partitioning the impacts among the co-products' in the biorefinery product portfolio. The aim of this study is to present the influence of the various allocation approaches on the LCA results of biorefinery products. The framework of this analysis systematically incorporates the steps of the LCA methodology as described in the ISO norms and estimates the impacts related with the products of interest, taking into account the contribution of the co- and by-products in the overall production path. For this reason, two wider approaches were adopted, the attributional which describes the impact of the production process itself from a retrospective point of view, and the consequential which focuses on the changes in the level of the output (as well as consumption and disposal) of a product, including market effects from increasing or decreasing demand for the study product, having therefore a more prospective point of view. Several scenarios which describe the possible options for handling those products, were developed and assessed based on different allocation methodologies, namely system expansion (substitution method) and partitioning methods according to the mass, thermal and economic values of the co-products. The estimation of the life cycle impacts of the processes was performed using the Global Warming Potential (GWP), Cumulative Energy Demand (CED) and RECIPE methodologies which provide an assessment of the burdens through the associated LCA indicators. The outcome of this approach provides a range of LCA metrics emphasizing at the variation of the results according to the followed allocation methods and to identify those properties of products (physical, economic, thermal) and system factors (processes to be substituted from the renewable ones, degree of utilization of co- and by-products from the markets etc.) which dominate the LCA results.
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| 10. |
- Karka, P., et al.
(författare)
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Predictive LCA - a systems approach to integrate LCA decisions ahead of design
- 2019
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; 46, s. 97-102
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Bio-refineries are promising production options of chemicals production, capable to produce a wide range of fuels and chemicals equivalent to the conventional fossil-based products. To establish bio-refineries as mature choices and achieve the commercialization of their technologies, the application of sustainable solutions during the design and development stages are crucial. The innovative character of bio-based production and therefore data availability and access on process modelling details, is a challenging point for decision makers to move towards this direction. Considering the environmental dimension out of the three aspects of sustainability, Life Cycle Assessment (LCA) is a suitable methodology for the evaluation of environmental impacts of bio-based processes because it highlights the stages with the greatest impact along a production chain. LCA studies require large amount of information, usually extracted from detailed flowsheets or from already completed pilot plants, making this procedure, costly, time consuming and not practical to act as a decision- support tool for the development of a bio-refinery. The aim of this study is to develop predictive models for the assessment of LCA metrics and use them to highlight sustainable design options for bio-refineries. Models require the least possible information, which can be obtained from chemistry - level data or early (conceptual) design stages. The modelling techniques used in this study are decision trees and Artificial Neural Networks (ANN), due to their easily interpretable structure and high computational capabilities, respectively. Models are based on the extraction of knowledge from a wide dataset for bio-refineries (it refers to 32 products that is, platform chemicals (e.g., syngas, sugars and lignin) and biofuels (e.g., biodiesel, biogas, and alcohols), starting from diverse biomass sources (e.g., wood chips, wheat straw, vegetable oil)). Input parameters include descriptors of the molecular structure and process related data which describe the production path of a study product. Models are able to predict LCA metrics which cover the most critical aspects of environmental sustainability such as cumulative energy demand (CED) and Climate Change (CC). The average classification errors for decision- tree models range between 17% (± 10%) to 38% (± 11%) whereas for ANN models the average R2cv values (coefficient of determination) range between 0.55 (± 0.42%) to 0.87 (± 0.07%). Demonstration of models is provided using case studies found in literature. Models are used to rank options in various design problems and support decisions on the selection of the most profitable option. Examples of such cases are the selection of the appropriate technology or feedstock to produce a desired product or the preliminary design of a bio-refinery configuration. The proposed approach provides a first generation of models that correlate available and easily accessed information to desirable output process parameters and assessment metrics and can be used as pre-screening tools in the development of innovative processes, ahead of detailed design, thus saving time and money.
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