1,352 research outputs found

    Metoda potpornih vektora u procjeni utjecaja karakteristika unaprijednih osmotskih membrana na zadržavanje organskih molekula

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    The forward osmosis (FO) process is currently being studied more despite other energy-consuming processes. In addition, several works show the performance of FO membranes as its major challenges, the study of the rejection of different molecules, energy consumption, and modelling of different objectives related to this process. The main purpose of our study was to evaluate the impact of the FO membranes characteristics on the rejection of organic molecules (neutral) by modelling of the latter. However, the current work deals with the application of Support Vector Machines (SVM) for predicting the rejection of organic molecules (53) by the FO membranes. In addition, the SVM model was compared with two other models: Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The coefficient of correlation (R) for the testing data was applied to display the best SVM model. The SVM model generated with Radial Basis Function (RBF) as the kernel function showed the best R value equal to 0.8526. MLR and ANN models had R values of 0.7630 and 0.8723, respectively.Proces unaprijedne osmoze (FO) trenutačno se učestalo proučava, a glavne su tematike zadržavanje različitih molekula, potrošnja energije i modeliranje samog procesa. Glavna svrha ovog istraživanja bila je, primjenom modeliranja, procijeniti utjecaj karakteristika FO membrana na zadržavanje neutralnih organskih molekula. Rad je fokusiran na primjenu metode potpornih vektora (engl. Support Vector Machines, SVM) za predviđanje zadržavanja organskih molekula (53) FO membranama. Razvijeni SVM model uspoređen je s dva druga modela: modelom umjetne neuronske mreže i modelom višestruke linearne regresije. SVM model generiran uz radijalnu baznu funkciju pokazao je najbolju vrijednost koeficijenta korelacije u iznosu 0,8526. Vrijednosti koeficijenta korelacije kod modela umjetne neuronske mreže i modela višestruke linearne regresije iznosile su 0,7630, odnosno 0,8723

    Applications of Mathematical Models in Engineering

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    The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

    Artificial intelligence in wind speed forecasting: a review

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    Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

    Strojno učenje i neuronske mreže u modeliranju zadržavanja polarnih farmaceutski aktivnih tvari nanofiltracijom i reverznom osmozom

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    The retention of polar pharmaceutical active compounds (PPhACs) by nanofiltration and reverse osmosis (NF/RO) membranes is of paramount importance in membrane separation processes. The retention of 21 PPhACs was correlated using artificial intelligence techniques: multi-layer perceptron (MLP), feedforward neural network with radial basis function (RBF), and support vector machine (SVM). A database of 541 retention values has been collected from the literature. The results showed a high predictive capacity of the MLP model for the retention of PPhACs by NF/RO with a very high correlation coefficient (R = 0.9714) and a very low root mean squared error (RMSE = 3.9139 %) for the entire data set. The comparison between the three models showed the superiority of the MLP model. The sensitivity analysis emphasised that the retention of PPhACs is governed by three interactions arranged in descending order: polarity interactions (hydrophobicity/hydrophilicity), electrostatic repulsion, and steric hindrance. This research suggests that the PPhACs retention on the NF/RO membrane strongly depends on the topological polar surface area.Zadržavanje polarnih farmaceutski aktivnih tvari (PPhAC) tijekom nanofiltracije i reverzne osmoze (NF/RO) od iznimne je važnosti u membranskim separacijskim procesima. Membransko zadržavanje 21 PPhAC-a korelirano je sa svojstvima PPhAC-a, karakteristikama membrane i uvjetima provedbe procesa filtracije. Pri tome su primijenjene tehnike umjetne inteligencije: višeslojni perceptron (MLP), neuronska mreža s radijalnom baznom funkcijom (RBF) i metoda potpornih vektora (SVM). Iz literature je prikupljena 541 vrijednost zadržavanja. Rezultati su pokazali visok kapacitet predviđanja MLP modela za cijeli skup podataka, s vrlo visokom vrijednošću koeficijenta korelacije (R = 0,9714) i vrlo niskom vrijednošću korijena srednje kvadratne pogreške (RMSE = 3,9139 %). Usporedba s preostala dva modela (RBF i SVM) pokazala je superiornost MLP modela. Analiza osjetljivosti ukazala je na to da zadržavanjem PPhAC-a upravljaju tri interakcije i to (padajućim redoslijedom): polarne interakcije (hidrofobnost/hidrofilnost), elektrostatsko odbijanje i steričke smetnje. Provedenoo istraživanje sugerira da zadržavanje PPhACs na NF/RO membrani snažno ovisi o topologiji polarne površine

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

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    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

    Prediction of Formation Conditions of Gas Hydrates Using Machine Learning and Genetic Programming

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    The formation of gas hydrates in the pipelines of oil, gas, chemical, and other industries has been a significant problem for many years because the formation of gas hydrates may block the pipelines. Hence, the knowledge of the phase equilibrium conditions of gas hydrate became necessary for the economic and safe working of oil, gas, chemical industries. Various thermodynamic approaches with various mathematical techniques are available for the prediction of formation conditions of gas hydrates. In this chapter, the authors have discussed the least square support vector machine and artificial neural network models for the prediction of stability conditions of gas hydrates and the use of genetic programming (GP) and genetic algorithm (GA) to develop a generalized correlation for predicting equilibrium conditions of gas hydrates

    Artifical neural network modelling of reverse osmosis process

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    With the increase in population and the scarcity of fresh water in the Middle East desalination has taken an important role in the provision of water for everyday use and for industrial purposes. Reverse osmosis water treatment process is of particular interest as it is one of the key processes in a desalination plant. The modelling of this process and the prediction of permeate flow is useful in better understanding the process. In the present study, an artificial neural network based model was developed based on plant data for the prediction of permeate flow performance. Plant data was collected and a number of variables determined. Principal component analysis was then carried and factor loadings obtained to identify the main variables. Once the main input variables were obtained a statistical analysis of the data was done in order to remove outliers present in the data. This was done because the presence of outliers in data to be analysed using ANN models renders the models ineffective in prediction of an output. Once the removal of outliers was done, the data was then analysed using the developed model. 1081 sets of data were originally used with twelve input variables. After principal component analysis was done the input variables were reduced to five with one output variable. With the removal of outliers 981 sets of data were obtained and these were then used in the model. The model was able to predict the output accurately with r2 at 0.97. Key factors determined from the process were that to obtain an optimum network one has to consider the epoch size, the transfer function, the learning rate and finally the number of nodes in the hidden layers. The number of hidden layers also had an effect on the overall prediction of the data. It is also important when using ANN models to obtain the correct input variables and to remove any outliers that are present in the data in order to be able to predict the output. The use of plant data severely limited optimisation of the process due to it already being heavily optimised

    Modeling the reserve osmosis processes performance using artificial neural networks

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    Una de las aplicaciones más importante de los procesos de filtración por membrana es en el área de tratamiento de agua por ultrafiltración, nanofiltración u ósmosis inversa. Entre los problemas más serios encontrados en estos procesos destaca la aparición de los fenómenos de ensuciamiento y envejecimiento de las membranas que limitan la eficacia de la operación tanto en la separación de los solutos, como en el flujo de permeado, afectando también el ciclo de vida de las membranas.Para reducir el coste de la producción y mejorar la robustez y eficacia de estos procesos es imprescindible disponer de modelos capaces de representar y predecir la eficiencia y el comportamiento de las membranas durante la operación. Una alternativa viable a los modelos teóricos, que presentan varias particularidades que dificultan su postulado, la constituyen los modelos basados en el análisis de los datos experimentales, entre cuales destaca el uso de las redes neuronales. Dos metodologías han sido evaluadas e investigadas, una constando en la caracterización de las interacciones entre las membranas y los compuestos orgánicos presentes en el agua de alimentación, y la segunda basada en el modelado de la dinámica de operación de las plantas de desalinización por ósmosis inversa.Relaciones cuantitativas estructura‐propiedad se han derivado usando redes neuronales de tipo back‐propagation, para establecer correlaciones entre los descriptores moleculares de 50 compuestos orgánicos de preocupación para la salud pública y su comportamiento frente a 5 membranas comerciales de ósmosis inversa, en términos de permeación, absorción y rechazo. Para reducir la dimensión del espacio de entrada, y para evitar el uso de la información redundante en el entrenamiento de los modelos, se han usado tres métodos para seleccionar el menor número de los descriptores moleculares relevantes entre un total de 45 que caracterizan cada molécula. Los modelos obtenidos se han validado utilizando un método basado en el balance de materia, aplicado no solo a los 50 compuestos utilizados para el desarrollo de los modelos, sino que también a un conjunto de 143 compuestos orgánicos nuevos. La calidad de los modelos obtenidos es prometedora para la extensión de la presente metodología para disponer de una herramienta comprensiva para entender, determinar y evaluar el comportamiento de los solutos orgánicos en el proceso de ósmosis inversa. Esto serviría también para el diseño de nuevas y más eficaces membranas que se usan en este tipo de procesos.En la segunda parte, se ha desarrollado una metodología para modelar la dinámica de los procesos de ósmosis inversa, usando redes neuronales de tipo backpropagation y Fuzzy ARTMAP y datos experimentales que proceden de una planta de desalinización de agua salobre Los modelos desarrollados son capaces de evaluar los efectos de los parámetros de proceso, la calidad del agua de alimentación y la aparición de los fenómenos de ensuciamiento sobre la dinámica de operación de las plantas de desalinización por osmosis inversa. Se ha demostrado que estos modelos se pueden usar para predecir el funcionamiento del proceso a corto tiempo, permitiendo de esta manera la identificación de posibles problemas de operación debidas a los fenómenos de ensuciamiento y envejecimiento de las membranas. Los resultados obtenidos son prometedores para el desarrollo de estrategias de optimización, monitorización y control de plantas de desalinización de agua salobre. Asimismo, pueden constituir la base del diseño de sistemas de supervisón capaces de predecir y advertir etapas de operación incorrecta del proceso por fallos en el mismo, y actuar en consecuencia para evitar estos inconvenientes.One of the more serious problems encountered in reverse osmosis (RO) water treatment processes is the occurrence of membrane fouling, which limits both operation efficiency (separation performances, water permeate flux, salt rejection) and membrane life‐time. The development of general deterministic models for studying and predicting the development of fouling in full‐scale reverse osmosis plants is burden due to the complexity and temporal variability of feed composition, diurnal variations, inability to realistically quantify the real‐time variability of feed fouling propensity, lack of understanding of both membrane‐foulants interactions and of the interplay of various fouling mechanisms. A viable alternative to the theoretical approaches is constituted by models developed based on direct analysis of experimental data for predicting process operation performance. In this regard, the use of artificial neural networks (ANN) seems to be a reliable option. Two approaches were considered; one based on characterizing the organic compounds passage through RO membranes, and a second one based on modeling the dynamics of permeate flow and separation performances for a full‐scale RO desalination plant.Organic solute sorption, permeation and rejection by RO membranes from aqueous solutions were studied via artificial neural network based quantitative structure‐property relationships (QSPR) for a set of 50 organic compounds for polyamide and cellulose acetate membranes. The separation performance for the organic molecules was modeled based on available experimental data achieved by radioactivity measurements to determine the solute quantity in feed, permeate and sorbed by the membrane. Solute rejection was determined from a mass balance on the permeated solution volume. ANN based QSPR models were developed for the measured organic sorbed (M) and permeated (P) fractions with the most appropriate set of molecular descriptors and membrane properties selected using three different feature selection methods. Principal component analysis and self‐organizing maps pre‐screening of all 50 organic compounds defined by 45 considered chemical descriptors were used to identify the models applicability domain and chemical similarities between the organic molecules. The ANN‐based QSPRs were validated by means of a mass balance test applied not only to the 50 organic compounds used to develop the models, but also to a set of 143 new compounds. The quality of the QSPR/NN models developed suggests that there is merit in extending the present compound database and extending the present approach to develop a comprehensive tool for assessing organic solute behavior in RO water treatment processes. This would allow also the design and manufacture of new and more performing membranes used in such processes.The dynamics of permeate flow rate and salt passage for a RO brackish water desalination pilot plant were captured by ANN based models. The effects of operating parameters, feed water quality and fouling occurrence over the time evolution of the process performance were successfully modeled by a back‐propagation neural network. In an alternative approach, the prediction of process performance parameters based on previous values was achieved using a Fuzzy ARTMAP analysis. The neural network models built are able to capture changes in RO process performance and can successfully be used for interpolation, as well as for extrapolation prediction, fact that can allow reasonable short time forecasting of the process time evolution. It was shown that using real‐time measurements for various process and feed water quality variables, it is possible to build neural network models that allow better understanding of the onset of fouling. This is very encouraging for further development of optimization and control strategies. The present methodology can be the basis of development of soft sensors able to anticipate process upsets
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