1,720,988 research outputs found
A simulation tool for analysis and design of reverse electrodialysis using concentrated brines
Get PDFReverse Electrodialysis (SGP-RE or RED) represents a viable technology for the conversion of the Salinity Gradient Power into electric power.
A comprehensive model is proposed for the RED process using sea or brackish water and concentrated brine as feed solutions. The goals were (i) reliably describing the physical phenomena involved in the process and (ii) providing information for optimal equipment design. For such purposes, the model has been developed at two different scales of description: a lower scale for the repeating unit of the system (cell pair), and a higher scale for the entire equipment (stack).
The model was implemented in a process simulator, validated against original experimental information and then used to investigate the influence of the main operating factors and on power output. Feed solutions of different salinities were also tested. A good matching was found between predictions and experiments for a wide range of inlet concentrations, flow rates and feed temperatures. Optimal feed conditions, for the adopted system geometry and membranes, have been found employing brackish water (0.08-0.1 M NaCl) as dilute and brine (4.5-5 M NaCl) as concentrate to generate the highest power density at 40°C temperature.
The model can be used to explore the full potential of the RED technology, especially for any investigation regarding the future scale-up of the process
Analysis and simulation of scale-up potentials in reverse electrodialysis
Get PDFThe Reverse Electrodialysis (RED) process has been widely accepted as a viable and promising technology to produce electric energy from salinity difference (salinity gradient power - e.g. using river water/seawater, or seawater and concentrated brines). Recent R&D efforts demonstrated how an appropriate design of the RED unit and a suitable selection of process conditions may crucially enhance the process performance. With this regard, a process simulator was developed and validated with experimental data collected on a lab-scale unit, providing a new modelling tool for process optimisation.
In this work, performed within the REAPower project (www.reapower.eu), a process simulator previously proposed by the same authors has been modified in order to predict the behaviour of a cross-flow RED unit. The model was then adopted to investigate the influence of the most important variables (i.e. solution properties and stack geometry) on the overall process performance. In particular, the use of different concentrations and flow rates for the feed streams have been considered, as well as different aspect ratios in asymmetric stacks. Moreover, the influence of the scaling-up a RED unit was investigated, starting from a 22x22 cm2 100 cell pairs lab-stack, and simulating the performance of larger stacks up to a 44x88 cm2 500 cell pairs unit.
Finally, different scenarios are proposed for a prototype-scale RED plant, providing useful indications for the technology scale-up towards 1 kW of power production, relevant to the installation of a real prototype plant in Trapani (Italy) being the final objective of the R&D activities of the REAPower project
An approach to process monitoring under probabilistic constraints
No full textOperators in chemical plants are confronted with several different measured process variables and parameters. Although the precision of measuring rose, individual measurements remained uncertain. This might affect real measurements such as temperature or pressure, where the measured value is more an expected value, with the real value within a range around it or also process dynamics, which hold exactly only under certain circumstances. Within the process monitoring and control, the operator has to take such uncertainties into account; on the one hand to not risk the violation of safety regulations, on the other hand to not use a too conservative control and give away product or quality. Even though an experienced and skilled operator might be able to handle single uncertain parameters and variables quiet efficiently, the outcome of multiple uncertain parameters is difficult. To handle multiple uncertain parameters simultaneously in optimisation, the concept of chance-constrained optimisation has been developed and extended over the last years. In this work, we present developed techniques of chance-constrained optimisation for process monitoring and control. It will allow to calculate potential key performance indicators out of uncertain variables and parameters, which can help operators in the decision making process. However, one drawback of using chance-constraints techniques is the required computation time for calculation. It requires a significant amount of individual calculations. Therefore, algorithmic improvements were required to meet the requirements of online monitoring and control. The talk will present the application of the developed chance-constrained approach on uncertain parameters in process monitoring and control, give an insight how the computing time improvements were fulfilled and show results of a practical evaluation
Discontinuities in mathematical modelling: origin, detection and resolution
Get PDFWhen modelling a chemical process, a modeller is usually required to handle a wide variations in time and/or length scales of its underlying differential equations by eliminating either the faster or slower dynamics. When compelled to deal with both and simultaneously simplify model structure, he/she is sometimes forced to make decisions that render the resulting model discontinuous. Discontinuities between adjacent regions, described by different equation sets, cause difficulties for ODE solvers. Two types exist for handling discontinuities in ODEs. Type I handles a discontinuity from the ODE solver side without paying any attention to the ODE model. This resolution to discontinuities suffer from underestimating the proper location of the discontinuity and thus results in solution errors. Type II discontinuity handlers resolve discontinuities at the model level by altering model structure or introducing bridging functions. This type of discontinuity handling has not been thoroughly explored in literature. I present a new hybrid (Type I and Type II) algorithm that eliminates integrator discontinuities through two steps. First, it determines the optimum switch point between two functions spanning adjacent or overlapping domains. The optimum switch point is determined by searching for a “jump point” that minimizes a discontinuity between adjacent/overlapping functions. Two resolution approaches exist. Approach I covers the entire overlap domain with an interpolating polynomial. Approach II relies on a moving vector to track a function trajectory during simulation run. Then, the discontinuity is resolved using an interpolating polynomial that joins the two discontinuous functions within a fraction of the overlap domain. The developed algorithm is successfully tested in models of a steady state chemical reactor exhibiting a bivariate discontinuity and a dynamic Pressure Swing Adsorption Unit exhibiting a univariate discontinuity in boundary conditions. Simulation results demonstrated a substantial increase in models' accuracy with a reduction in simulation runtime
Defining a comprehensive methodology for sustainability assessment of mega-event projects
Get PDFMega-event projects such as the Olympic Games or FIFA World Cup are unique large-scale projects that involve complex planning process, vast array of stakeholders and substantial capital investment. They attract global media attention and tourism to a host city. However, the success of a mega-event is not measured only in terms of its organisation and staging. It is crucial to create a sustainable positive post-event legacy because this is where the most of the long-term impacts will occur. Planning of such projects is a complicated process that requires consideration of multiple economic, environmental and social aspects and the trade-offs between them. The main objective of this work is to develop a comprehensive framework that can assist decision makers with assessment of the alternative site design scenarios in order to identify the optimum solution. A case study based on the London Olympic Park is applied to test the feasibility of the proposed framework. Stakeholders’ engagement in a mega-event project planning is a prerequisite for its success. This work demonstrates how a multi-criteria decision analysis (MCDA) tool can be applied to analyse and quantify the views of different stakeholder groups and identify the design features which are considered the most important by the majority of stakeholders. The environmental assessment framework includes a combination of computational models which evaluate and optimise the total emissions resulting from the transportation, materials, water and energy use, and a series of life cycle assessment (LCA) models which estimate environmental burdens resulting from municipal solid waste (MSW) treatment systems. The results of the assessment provide valuable information for the decision makers in terms of the amount of materials and energy used and related environmental burdens for each scenario. Optimisation models can determine ‘the optimum’ solution for each scenario which can serve as a performance benchmark during the planning process
A computational model of hepatic energy metabolism: Understanding the role of zonation in the development and treatment of non-alcoholic fatty liver disease (NAFLD).
Get PDFNon-alcoholic fatty liver disease (NAFLD) is a highly prevalent condition associated with increased risk of liver failure, diabetes and numerous further conditions. In NAFLD, lipid build-up and the resulting damage occurs most severely in hepatocytes at the pericentral end of the capillaries (sinusoids) which supply the cells with blood [1-3]. Due to the complexity of studying individual regions of the sinusoids, the causes of this zone specificity and its implications on treatment have largely been ignored in previous research. In this study, a computational model of liver glucose and lipid metabolism was developed which includes zone-dependent enzyme expression. This model was then used to study the development of NAFLD across the sinusoid. By simulating insulin resistance and high intake diets leading to the development of steatosis in the model, we propose a novel mechanism leading to pericentral steatosis in NAFLD patients. Sensitivity analysis on the rate parameters in the model was then used to highlight key inter-individual variations in hepatic metabolism with the largest effect on steatosis development. Secondly, the model, in combination with cell culture experiments, was used to assess potential drug targets for clearing steatosis across the sinusoid without disrupting other aspects of metabolism. Adverse effects were highlighted when targeting (stimulating or inhibiting through altering the rate constants) for most processes in the model, and these were largely validated in the hepatocyte-like cell culture line through the addition of small molecule inhibitors. However, inhibition of lipogenesis combined with stimulation of β-oxidation was predicted to clear steatosis, reduce hepatic FFA levels, reduce excess ETC flux and increase hepatic ATP concentrations across the sinusoid without causing adverse effects elsewhere in metabolism. Furthermore, in the cell culture model, inhibition of lipogenesis combined with stimulation of β-oxidation using acetyl-CoA carboxylase inhibitor TOFA, resulted in clearance of steatosis, improved cell viability, reduced oxidative stress and increased mitochondrial function
A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers
Get PDFThe challenges posed by smart manufacturing for the process industries and for process systems engineering (PSE) researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, but benchmarking would give greater confidence. Technical challenges confronting process systems engineers in developing enabling tools and techniques are discussed regarding flexibility and uncertainty, responsiveness and agility, robustness and security, the prediction of mixture properties and function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to drive agility will require tackling new challenges, such as how to ensure the consistency and confidentiality of data through long and complex supply chains. Modeling challenges also exist, and involve ensuring that all key aspects are properly modeled, particularly where health, safety, and environmental concerns require accurate predictions of small but critical amounts at specific locations. Environmental concerns will require us to keep a closer track on all molecular species so that they are optimally used to create sustainable solutions. Disruptive business models may result, particularly from new personalized products, but that is difficult to predict
Improvement of crude oil refinery gross margin using a NLP model of a crude distillation unit system
No full textThis work presents a Non Linear Programming (NLP) model developed to optimize simultaneously a crude oil distillation unit (CDU) system and several cases of application run in a refinery as well. This model optimizes feedstock composition and operational conditions for a CDU System (ECOPETROL S.A.). The NLP Model uses a Metamodeling approach so as to represent Atmospheric Distillation Towers (ADT). The Vacuum Distillation Towers (VDT) are implemented assuming perfect separation (assay cuttings). The defined objective function is given by an economic profit. The CDU system consists basically of five industrial units and fourteen Colombian Crude Oils. Each Metamodel uses as independent variables: crude oil flow rates, operational conditions, Jet EBP, and Diesel T95% from ASTM D-86 distillation curve. The output variables of the Metamodels are product flows, temperatures, and qualities. The developed NLP model was implemented in GAMS. The time needed for its solution is around 60s while using the CONOPT solver. The NLP model results were successfully applied to a Colombian refinery for 3 consecutive weeks. The model was able to find the best use of installed equipments in CDUs through the preparation of a crude oil charge quasi-constant quality without matter the time period of the optimization. In each week, optimal crude oil flow rates towards each CDU (like new scenarios implemented in the refinery) were evaluated in a refinery global simulator with all downstream refining schemes in order to calculate the Refinery Gross Margin (RGM). In each analyzed case, the obtained RGM for new crude oil feeds was however better than that case without optimization with a economic benefit of up to 0.043 US 3.870.000 per year. This shows the effectiveness of a CDU NLP model within short term planning in the petroleum industry
Model-based design of experiments for model identification using closed-loop set-point response
No full textIn this work, a new approach to model identification based on model-based experimental design is presented. In the proposed strategy, system identification relies on a closed-loop set-point response. For this purpose, experiments are first exemplarily executed with a P-controller. Therefore, in this specific case only one design variable is considered that is represented by the controller gain. In order to validate our approach and demonstrate the benefits of the proposed strategy different scenarios are simulated
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