Modeling and optimization of spinning conditions for polyethersulfone hollow fiber membrance fabrication using non-dominated sorting genetic algorithm-II

Optimization of spinning conditions plays a key role in the development of high performance asymmetric hollow fiber membranes. However, from previous studies, in solving these spinning condition optimization problems, they were handled mostly by using an experimentation that varied one of the independent spinning conditions and fixed the others. The common problem is the preparation of hollow fiber membranes that cannot be performed effectively due to inappropriate settings of the spinning conditions. Moreover, complexities in the spinning process have increased where the interaction effects between the spinning conditions with the presence of multiple objectives also affect the optimal spinning conditions. This is one of the main reasons why very little work has been carried out to vary spinning conditions simultaneously. Hence, in order to address these issues, this study focused on a non-dominated sorting genetic algorithm-II (NSGA-II) methodology to optimize the spinning conditions during the fabrication of polyethersulfone (PES) ultrafiltration hollow fiber membranes for oily wastewater treatment to maximize flux and rejection. Spinning conditions that were investigated were dope extrusion rate (DER), air gap length (AGL), coagulation bath temperature (CBT), bore fluid ratio (BFR), and post-treatment time (PT). First, the work was focused on predicting the performance of hollow fiber membranes by considering the design of experiments (DOE) and statistical regression technique as an important approach for modeling flux and rejection. In terms of experiments, a response surface methodology (RSM) and a central composite design (CCD) were used, whereby the factorial part was a fractional factorial design with resolution V and overall, it consisted of a combination of high levels and low levels, center points, as well as axial points. Furthermore, the regression models were generated by employing the Design Expert 6.0.5 software and they were found to be significant and valid. Then, the regression models obtained were proposed as the objective functions of NSGA-II to determine the optimal spinning conditions. The MATLAB software was used to code and execute the NSGA-II. With that, a non-dominated solution set was obtained and reported. It was discovered that the optimal spinning conditions occurred at a DER of 2.20 cm3/min, AGL of 0 cm, CBT of 30 °C, BFR (NMP/H2O) of 0/100 wt.%, and PT of 6 hour. In addition, the membrane morphology under the influence of different spinning conditions was investigated via a scanning electron microscope (SEM). The proposed optimization method based on NSGA-II offered an effective way to attain simple but robust solutions, thus providing an efficient production of PES ultrafiltration hollow fiber membranes to be used in oily wastewater treatment. Therefore, the optimization results contributed by NSGA-II can assist engineers and researchers to make better spinning optimization decisions for the membrane fabrication process

A sustainable ultrafiltration of sub-20 nm nanoparticles in water and isopropanol: experiments, theory and machine learning

This research focused on ultrafiltration (UF) for particles down to 2 nm against membranes with larger pore size in water and IPA, which has the potential to save up to 90% of energy. This study developed electrospray (ES) - scanning mobility particle sizer (SMPS) method to fast and effective measure retention efficiencies for small particles (ZnS, Au and PSL) on polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and polycarbonate (PCTE) in different liquids. Theoretical models that could quantitatively explain the experimental results for small particles in medium-polarity organic solvents were also developed. Results showed that the highest efficiency was up to ~80% with 10 nm Au nanoparticle challenged on 100 nm rated PTFE, which demonstrated the feasibility of the proposed sustainable UF. The theoretical models were validated by experimental results and indicated that a higher efficiency was possible by enhancing material properties of membranes, particles, or liquids. Therefore, optimization on filtration condition was performed. A hybrid artificial neural network (ANN) and particle swarm optimization algorithm (PSO) models was firstly applied in this case. The dataset includes all the experimental results and some additional calculated retention efficiencies. Optimization parameters include membrane zeta potential, pore size, particle size, particle zeta potential, and Hamaker constant. The ANN model provided highly correlated predicted values with target values. The PSO model showed that a filtration efficiency of 99.9% could be achieved by using a 52.2 nm filter with a -20.3 mV zeta potential, 5.5 nm nanoparticles with a 41.4 mV zeta potential, and a combined Hamaker constan

Developing tools for determination of parameters involved in CO₂ based EOR methods

To mitigate the effects of climate change, CO₂ reduction strategies are suggested to lower anthropogenic emissions of greenhouse gasses owing to the use of fossil fuels. Consequently, the application of CO₂ based enhanced oil recovery methods (EORs) through petroleum reservoirs turn into the hot topic among the oil and gas researchers. This thesis includes two sections. In the first section, we developed deterministic tools for determination of three parameters which are important in CO₂ injection performance including minimum miscible pressure (MMP), equilibrium ratio (Kᵢ), and a swelling factor of oil in the presence of CO₂. For this purposes, we employed two inverse based methods including gene expression programming (GEP), and least square support vector machine (LSSVM). In the second part, we developed an easy-to-use, cheap, and robust data-driven based proxy model to determine the performance of CO₂ based EOR methods. In this section, we have to determine the input parameters and perform sensitivity analysis on them. Next step is designing the simulation runs and determining the performance of CO₂ injection in terms of technical viewpoint (recovery factor, RF). Finally, using the outputs gained from reservoir simulators and applying LSSVM method, we are going to develop the data-driven based proxy model. The proxy model can be considered as an alternative model to determine the efficiency of CO₂ based EOR methods in oil reservoir when the required experimental data are not available or accessible

Articles indexats publicats per investigadors del Campus de Terrassa: 2015

Aquest informe recull els 284 treballs publicats per 218 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2015Postprint (published version

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Book of abstracts of the 10th International Chemical and Biological Engineering Conference: CHEMPOR 2008

This book contains the extended abstracts presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, over 3 days, from the 4th to the 6th of September, 2008. Previous editions took place in Lisboa (1975, 1889, 1998), Braga (1978), Póvoa de Varzim (1981), Coimbra (1985, 2005), Porto (1993), and Aveiro (2001). The conference was jointly organized by the University of Minho, “Ordem dos Engenheiros”, and the IBB - Institute for Biotechnology and Bioengineering with the usual support of the “Sociedade Portuguesa de Química” and, by the first time, of the “Sociedade Portuguesa de Biotecnologia”. Thirty years elapsed since CHEMPOR was held at the University of Minho, organized by T.R. Bott, D. Allen, A. Bridgwater, J.J.B. Romero, L.J.S. Soares and J.D.R.S. Pinheiro. We are fortunate to have Profs. Bott, Soares and Pinheiro in the Honor Committee of this 10th edition, under the high Patronage of his Excellency the President of the Portuguese Republic, Prof. Aníbal Cavaco Silva. The opening ceremony will confer Prof. Bott with a “Long Term Achievement” award acknowledging the important contribution Prof. Bott brought along more than 30 years to the development of the Chemical Engineering science, to the launch of CHEMPOR series and specially to the University of Minho. Prof. Bott’s inaugural lecture will address the importance of effective energy management in processing operations, particularly in the effectiveness of heat recovery and the associated reduction in greenhouse gas emission from combustion processes. The CHEMPOR series traditionally brings together both young and established researchers and end users to discuss recent developments in different areas of Chemical Engineering. The scope of this edition is broadening out by including the Biological Engineering research. One of the major core areas of the conference program is life quality, due to the importance that Chemical and Biological Engineering plays in this area. “Integration of Life Sciences & Engineering” and “Sustainable Process-Product Development through Green Chemistry” are two of the leading themes with papers addressing such important issues. This is complemented with additional leading themes including “Advancing the Chemical and Biological Engineering Fundamentals”, “Multi-Scale and/or Multi-Disciplinary Approach to Process-Product Innovation”, “Systematic Methods and Tools for Managing the Complexity”, and “Educating Chemical and Biological Engineers for Coming Challenges” which define the extended abstracts arrangements along this book. A total of 516 extended abstracts are included in the book, consisting of 7 invited lecturers, 15 keynote, 105 short oral presentations given in 5 parallel sessions, along with 6 slots for viewing 389 poster presentations. Full papers are jointly included in the companion Proceedings in CD-ROM. All papers have been reviewed and we are grateful to the members of scientific and organizing committees for their evaluations. It was an intensive task since 610 submitted abstracts from 45 countries were received. It has been an honor for us to contribute to setting up CHEMPOR 2008 during almost two years. We wish to thank the authors who have contributed to yield a high scientific standard to the program. We are thankful to the sponsors who have contributed decisively to this event. We also extend our gratefulness to all those who, through their dedicated efforts, have assisted us in this task. On behalf of the Scientific and Organizing Committees we wish you that together with an interesting reading, the scientific program and the social moments organized will be memorable for all.Fundação para a Ciência e a Tecnologia (FCT

Prediction of separation characteristics of complexing – microfiltration process using artificial neural networks

Zagađenost životne sredine, pre svega vode, teškim metalima predstavlja ekološki problem širom sveta. Povećana koncentracija teških metala u komunalnim i industrijskim otpadnim vodama predstavlja ozbiljnu pretnju s obzirom da se metali ne mogu razgraditi u prirodi i da neki mogu imati toksične efekte na biljke i životinje kao i na čoveka. Da bi se njihova koncentracija smanjila na adekvatan nivo definisan zakonskom regulativom, neophodno je primeniti metode prečišćavanja pre ispuštanja u recipijente. Membranski procesi se sve više koriste u oblasti zaštite životne sredine kao i za pripremu i preradu vode za potrebe prehrambene i farmaceutske industrije, petrohemije i dr. Glavne prednosti ovih procesa su mala energetska potrošnja, velika efikasnost, pouzdanost i mala količina otpada. Kompleksirajuće-mikrofiltracioni proces je hibridna membranska separaciona metoda koja se primenjuje za uklanjanje jona teških metala iz vode. Zasnovana je na konceptu da se mali joni metala, koji bi prolazili kroz pore mikrofiltracione membrane, ukrupne kompleksiranjem/vezivanjem sa makromolekulima. Na ovaj način nastale čestice postaju veće od pora na membrani i bivaju zadržane na površini i na taj način uklonjene iz vode. Da bi ovaj proces bio primenljiv u praksi potrebno je ostvariti visok fluks i protok prečišćene vode i visok koeficijent zadržavanja jona metala. Stoga bi mogućnost predviđanja separacionih karakteristika u sistemu bila od izuzetnog značaja za primenu kompleksirajuće-mikrofiltracionog procesa. Za predviđanje vrednosti fluksa razvijeni su određeni matematički modeli kao što su model procesa kontrolisanog pritiskom, model teorije gela, model osmotskog pritiska, model otpora. Ipak, nijedan od ovih modela nije zadovoljavajući i ne može opisati sve oblasti u kojima se odvija proces. Uspešnost kompleksiranja kao i sprečavanje pada fluksa zavisi od uslova procesa i različitih radnih parametara. Međusobne veze u sistemu jon metala – makromolekul – membrana su nelinearne i nedovoljno definisane. Veštačke neuronske mreže privlače sve više pažnje kada matematički linearni modeli nisu primenljivi, jer se mogu koristiti ulazni podaci sa nelinearnim odnosima umesto fizički zavisnih odnosa ulaznih vrednosti. Glavni cilj ove disertacije bio je razvoj i optimizacija modela primenom veštačkih neuronskih mreža za predviđanje separacionih karakteristika kompleksirajuće-mikrofiltracionog procesa uklanjanja teških metala iz vode. Istraživanja u okviru ove disertacije bila su podeljena u dva segmenta. U prvom delu, eksperimentalno su ispitani parametri koji mogu uticati na koeficijent zadržavanja i fluks permeata kao što su radni pritisak, pH vrednost rastvora, početna koncentracija jona teških metala, koncentracija agensa za kompleksiranje i prisustvo jedinjenja na bazi aminokiselina kao koliganda. Utvrđeno je da najveći uticaj na proces imaju pritisak, pH vrednost i koncentracija kompleksirajućeg agensa...Environmental pollution, primarily water, with heavy metals is an environmental problem around the world. The increased concentration of heavy metals in municipal and industrial wastewater represent a serious threat as metals cannot be degraded in nature and some can have toxic effects on plants and animals as well as humans. In order to reduce their concentration to an adequate level defined by legislation, it is necessary to apply purification methods before discharge into recipients. Membrane processes are increasingly used in the field of environmental protection as well as for the preparation and processing of water for the food and pharmaceutical industry, petrochemistry, etc. The main advantages of these processes are low energy consumption, high efficiency, reliability and small amount of waste. The complexing-microfiltration process is a hybrid membrane separation method used to remove heavy metal ions from water. It is based on the concept that small metal ions, which would pass through the pores of the microfiltration membrane, are enlarged by complexation / binding with macromolecules. The particles formed in this way become larger than the pores on the membrane and are retained on the surface and thus removed from the water. In order for this process to be applicable in practice, it is necessary to achieve a high flux and flow of purified water and a high retention coefficient of metal ions. Therefore, the ability to predict the separation characteristics in the system would be extremely important for the application of the complexing-microfiltration process. To predict the flux value, certain mathematical models have been developed, such as the model of the process controlled by pressure, the model of gel theory, the model of osmotic pressure, the model of resistance. However, none of these models is satisfactory and cannot describe all the areas in which the process takes place. The success of complexation as well as the prevention of flux drop depends on the process conditions and various operating parameters. The interactions in the ion metal - macromolecule - membrane system are nonlinear and insufficiently defined. Artificial neural network (ANN) models are attracting increasing attention for use in situations where mathematical linear models are not applicable because they may have nonlinear relationships between variables instead of physical relationships of input values. The main goal of this dissertation was to develop and optimize a model of an artificial neural network to predict the separation characteristics of the complexing-microfiltration process of removing heavy metals from water. The research within this dissertation was divided into two segments. In the first part, parameters that may affect the retention coefficient and permeate flux such as working pressure, pH value of the solution, initial concentration of heavy metal ions, concentration of complexing agents and the presence of compounds based on amino acid as coligand. The greatest influence on the process was found to have the pressure, pH value and concentration of the complexing agent..

Development of Novel Pectinase and Xylanase Juice Clarification Enzymes via a Combined Biorefinery and Immobilization Approach

Hydrolytic enzymes, such as pectinase and xylanase, maybe harnessed for numerous industrial applications in food industry. Therefore, economic factors such as achievement of optimum yieldsandoverall production costs, in addition to biocatalyst instability,are the main obstacles tothe industrial production and exploitation of a enzymes. For example, microbially-derived enzymes are typically produced in fermenters using expensive growth media, which may account for 30 to 40% of the production cost, and such expense may be compounded further by downstream processing operations.To counter such disadvantages, the major trend in industrial utilization of enzymes in cost-sensitive processes has been to immobilize such biocatalysts on a solid support, thereby mediating the key advantage of reusability.A complementary approachin recent years has been to target reduction inupstream processing costs in enzyme production by incorporation of negative cost raw materialsinto the medium composition, Food waste such as Brewers’ spent grain (BSG) constitutes anenvironmental problem in Ireland buthas the potentialtoprovidea continuous and renewable feedstock (carbon source) for production of industrial enzymes, thereby reducing production costs. However, a significant barrier to the exploitation of lignocellulose is the recalcitrant nature of the biomass, which usually requires expensive pre-treatment to release sugars.The key goal of this study was to pursue an integrated upstream and downstream approach to develop novel immobilized enzyme preparations for the juice industry. The aim of the project was achievedthrough the following measures:•Screening and isolation of microrganisms which produced xylanases and pectinases possessing a physico-chemical profile of relevance to the fruit juice sector. •Development of an optimal pretreatment strategy for BSGthat would enhance their suitability as a carbon source for the upstream processing of the microbe which produced the enzymes. •Optimisation ofthe upstream processing for the production of as the pectinaseand xylanasecatalysts.•Investigation of a novel method for enzyme immobilization, and establishment of optimal process performance characteristics for use in juice clarificatio

Performance assessment of Surrogate model integrated with sensitivity analysis in multi-objective optimization

This Thesis develops a new multi-objective heuristic algorithm. The optimum searching task is performed by a standard genetic algorithm. Furthermore, it is assisted by the Response Surface Methodology surrogate model and by two sensitivity analysis methods: the Variance-based, also known as Sobol’ analysis, and the Elementary Effects. Once built the entire method, it is compared on several multi-objective problems with some other algorithms

Articles publicats per investigadors de l'ETSEIB indexats al Journal Citation Reports: 2013

Informe que recull els 297 treballs publicats per 203 investigadors de l'Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) en revistes indexades al Journal Citation Reports durant l’any 2013.Postprint (published version