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"Manenti, Flavio"
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Comparison of Desalination Technologies Using Renewable Energy Sources with Life Cycle, PESTLE, and Multi-Criteria Decision Analyses
Nowadays, desalination continues to expand globally, which is one of the most effective solutions to solve the problem of the global drinking water shortage. However, desalination is not a fail-safe process and has many environmental and human health consequences. This paper investigated the desalination procedure of seawater with different technologies, namely, multi-stage flash distillation (MSF), multi-effect distillation (MED), and reverse osmosis (RO), and with various energy sources (fossil energy, solar energy, wind energy, nuclear energy). The aim was to examine the different desalination technologies’ effectiveness with energy sources using three assessment methods, which were examined separately. The life cycle assessment (LCA), PESTLE, and multi-criteria decision analysis (MCDA) methods were used to evaluate each procedure. LCA was based on the following impact analysis and evaluation methods: ReCiPe 2016, IMPACT 2002+, and IPCC 2013 GWP 100a; PESTLE risk analysis evaluated the long-lasting impact on processes and technologies with political, economic, social, technological, legal, and environmental factors. Additionally, MCDA was based on the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to evaluate desalination technologies. This study considered the operational phase of a plant, which includes the necessary energy and chemical needs, which is called “gate-to-gate” analysis. Saudi Arabia data were used for the analysis, with the base unit of 1 m3 of the water product. As the result of this study, RO combined with renewable energy provided outstanding benefits in terms of human health, ecosystem quality, and resources, as well as the climate change and emissions of GHGs categories.
Journal Article
Process Integration of Green Hydrogen: Decarbonization of Chemical Industries
Integrated water electrolysis is a core principle of new process configurations for decarbonized heavy industries. Water electrolysis generates H2 and O2 and involves an exchange of thermal energy. In this manuscript, we investigate specific traditional heavy industrial processes that have previously been performed in nitrogen-rich air environments. We show that the individual process streams may be holistically integrated to establish new decarbonized industrial processes. In new process configurations, CO2 capture is facilitated by avoiding inert gases in reactant streams. The primary energy required to drive electrolysis may be obtained from emerging renewable power sources (wind, solar, etc.) which have enjoyed substantial industrial development and cost reductions over the last decade. The new industrial designs uniquely harmonize the intermittency of renewable energy, allowing chemical energy storage. We show that fully integrated electrolysis promotes the viability of decarbonized industrial processes. Specifically, new process designs uniquely exploit intermittent renewable energy for CO2 conversion, enabling thermal integration, H2 and O2 utilization, and sub-process harmonization for economic feasibility. The new designs are increasingly viable for decarbonizing ferric iron reduction, municipal waste incineration, biomass gasification, fermentation, pulp production, biogas upgrading, and calcination, and are an essential step forward in reducing anthropogenic CO2 emissions.
Journal Article
Computationally‐Efficient Environmental and Economic Multi‐Objective Optimization of a Methanol Production Process via Surrogate Modeling
Process simulation is a powerful tool in the Process Systems Engineering (PSE) field, in particular for optimization tasks. However, the computational times involved in these activities may become prohibitive for complex processes. As an alternative, data‐driven strategies, such as surrogate models, have been widely adopted. Surrogate models are typically trained on data generated from specifically designed simulation runs. The computational efficiency of these designs has been addressed in the literature by minimizing the total number of simulations. However, the execution time of each simulation may be potentially reduced by shortening the transient period between consecutive simulations, for example, by minimizing the Euclidean distance between them. Sorting the simulations of the design to minimize the total traveled distance describes a typical Traveling Salesman Problem (TSP) scenario. This work analyzes the effect of four random and sorted one‐shot experimental designs, composed of 50 samples, on the surrogate model training and surrogate‐based optimization of a methanol synthesis process: DoE 1) Latin Hypercube (LHS), DoE 2) maxmin LHS, DoE 3) maxmin LHS sorted with nearest neighbors, and DoE 4) maxmin LHS sorted with 2‐opt. Results showed that sorted DoEs improved the surrogate model accuracy by reducing its relative error by 0.3%. In addition, the overall computational time required diminished by around 14%. The most efficient experimental design was DoE 4, which was used to train a model later employed to optimize the OPEX and CO 2 $$ {}_2 $$emissions of the methanol process, resulting in reductions of 15.0% and 11.4%, respectively. Latin Hypercube Sampling (LHS), widely used in surrogate modeling, was enhanced by applying a maxmin criterion and sorting the samples via a Traveling Salesman Problem (TSP) strategy. This reduced the Euclidean travel distance during simulations, improved space coverage, boosted model accuracy by 0.13%, and cut computational time by 14%
Journal Article
Valorisation of Coffee Roasting By-Products: Recovery of Silverskin Fat By Supercritical CO2 Extraction
Recovery of agro and food-industrial waste and their valorisation via green technologies can help to outline new concepts of industrial strategies. In this contest, a fat enriched of added-value components was extracted from coffee silverskin by applying a supercritical fluid extraction technique (sc-CO
2
). An appropriate modulation of process parameters like temperature (T = 35, 50, 60 °C) and pressure (p = 200–300 bar) influences the fat yield and the chemical composition, opening the way for targeted extraction. The extraction time, the organic solvent use and the energy consume were reduced compared to Soxhlet. Moreover, a mathematical model was constructed based on the experimental data collected, employed apparatus, and physico-chemical characteristics of biomass, pointing to a possible industrial scale-up. The experimental results are accompanied by a preliminary cost of manufacturing (COM), highlighting how the high investment for the apparatus is compensated by several benefits.
Graphic Abstract
Journal Article
Nonlinear Systems and Optimization for the Chemical Engineer
This third book in a suite of four practical guides is an engineer's companion to using numerical methods for the solution of complex mathematical problems.The required software is provided by way of the freeware mathematical library BzzMath that is developed and maintained by the authors.
eBook
30th European Symposium on Computer Aided Chemical Engineering
30th European Symposium on Computer Aided Chemical Engineering, Volume 47 contains the papers presented at the 30th European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Milan, Italy, May 24-27, 2020.
eBook
Differential and Differential-Algebraic Systems for the Chemical Engineer
Engineers and other applied scientists are frequently faced with models of complex systems for which no rigorous mathematical solution can be calculated. To predict and calculate the behaviour of such systems, numerical approximations are frequently used, either based on measurements of real life systems or on the behaviour of simpler models. This is essential work for example for the process engineer implementing simulation, control and optimization of chemical processes for design and operational purposes. This fourth in a suite of five practical guides is an engineer's companion to using numerical methods for the solution of complex mathematical problems. It explains the theory behind current numerical methods and shows in a step-by-step fashion how to use them. The volume focuses on differential and differential-algebraic systems, providing numerous real-life industrial case studies to illustrate this complex topic. It describes the methods, innovative techniques and strategies that are all implemented in a freely available toolbox called BzzMath, which is developed and maintained by the authors and provides up-to-date software tools for all the methods described in the book. Numerous examples, sample codes, programs and applications are taken from a wide range of scientific and engineering fields, such as chemical engineering, electrical engineering, physics, medicine, and environmental science. As a result, engineers and scientists learn how to optimize processes even before entering the laboratory. With additional online material including the latest version of BzzMath Library, installation tutorial, all examples and sample codes used in the book and a host of further examples.
eBook
Combustion analysis of a light duty diesel engine using oxygen-enriched and humidified combustion air
The present work presents the results of 3D CFD combustion simulations of a current production 4-cylinder turbocharged Diesel engine using oxygen-enriched and humidified combustion air. Enriched Air (EA) is supposed to be produced by desorption from water, exploiting the different Henry constants of N 2 and O 2 . Simulation results show that EA permits to increase the engine thermal efficiency (up to 10%) and drastically reduces soot emissions but increases in-cylinder peak pressure and NO x emissions. Combustion air humidification helps to reduce NO x increment, without losing the advantage in terms of thermal efficiency and in soot reduction, even if NO x emissions cannot be reported to the base case values.
Journal Article
Optimal Cleaning Cycle Scheduling under Uncertain Conditions: A Flexibility Analysis on Heat Exchanger Fouling
Fouling is a substantial economic, energy, and safety issue for all the process industry applications, heat transfer units in particular. Although this phenomenon can be mitigated, it cannot be avoided and proper cleaning cycle scheduling is the best way to deal with it. After thorough literature research about the most reliable fouling model description, cleaning procedures have been optimized by minimizing the Time Average Losses (TAL) under nominal operating conditions according to the well-established procedure. For this purpose, different cleaning actions, namely chemical and mechanical, have been accounted for. However, this procedure is strictly related to nominal operating conditions therefore perturbations, when present, could considerably compromise the process profitability due to unexpected shutdown or extraordinary maintenance operations. After a preliminary sensitivity analysis, the uncertain variables and the corresponding disturbance likelihood were estimated. Hence, cleaning cycles were rescheduled on the basis of a stochastic flexibility index for different probability distributions to show how the uncertainty characterization affects the optimal time and economic losses. A decisional algorithm was finally conceived in order to assess the best number of chemical cleaning cycles included in a cleaning supercycle. In conclusion, this study highlights how optimal scheduling is affected by external perturbations and provides an important tool to the decision-maker in order to make a more conscious design choice based on a robust multi-criteria optimization.
Journal Article