1,721,210 research outputs found
Modelling of Electrodialysis with Bipolar Membranes processes using hybrid model supported by Artificial Neural Networks
No full textElectrodialysis with bipolar membranes appears to be a promising process for thesimultaneous production of acid and alkaline solutions. This process can be integratedinto circular economy approaches for waste streams valorisation or utilised alone,especially in remote areas, to reduce transportation, storage and handling of thesehazardous chemicals. However, there is a lack of information on large-scale units in realoperational environments, and, as a result, there are no validated modelling tools that canbe used for its design, optimisation, simulation and control. The aim of the present PhDthesis is to design and test a semi-industrial electrodialysis with bipolar membranes unitand develop versatile modelling tools that can be adopted for the above-mentionedapplication. The experimental investigation focused on evaluating well established andnew process configurations and operational schemes, as well as testing the process insidean integrated treatment chain to valorise a seawater brine. The collected data were utilisedto develop the modelling tools. Firstly, a model with a first principles approach wasobtained and validated to simulate large-scale units also with complex stack configuration(i.e., internal staging). The model was subsequently modified to account also fornonstationary operations. In addition, the possibility of adopting innovative modellingtools was considered. For the first time, artificial neural network models were used tosimulate the Electrodialysis with bipolar membranes process. Finally, the combination offirst principles and data-driven models was considered to develop innovative hybridmodels with superior performance compared to the two types of models used alone. Theobtained results can guide the selection of the most appropriate process configurationdepending on the applications, while the proposed models can be selected as realisabletools to predict the process behaviour depending on the application
Membrane Technologies for the Water Treatment. Part 1: Basic Principles
No full textMembrane technologies have become a key player among technologies
for the treatment and purification of water streams. Common applications are (i)
filtration for drinking water polishing; (ii) filtration as tertiary treatment in wastewater
treatment plants; (iii) reverse osmosis for freshwater production via desalination
of brackish and seawater, etc. All membrane technologies are based on the use of
selective membranes packed into membrane modules arranged into module racks,
eventually constituting a plant. The features of membrane materials, module geometry,
and plant configuration are fundamental to understanding the behaviour and
operational performance of the membrane separation process. In this chapter, an
overview of the fundamentals of membrane technologies is provided, spanning from
basic definitions, membrane materials, and fabrication techniques to module development
and transport phenomena governing the most important type of applications
for water treatment
A comprehensive multi-scale model for bipolar membrane electrodialysis (BMED)
Full text linkBipolar membrane electrodialysis (BMED) is a technology combining solute and solvent dissociation to produce chemicals. In the recent decades, it has been typically studied for the production of valuable acid and base solutions from salt streams. Although many works have been devoted to the experimental investigation of BMED, only a few efforts have focused on its mathematical modelling. In the present work, a comprehensive process model based on a multi-scale approach with distributed parameters is presented for the first time. Five models related to four different dimensional scales were fully integrated to form a comprehensive tool. The integrated model was developed by using the process simulator gPROMS Model builder and was based on a semi-empirical approach combining high prediction accuracy and low computational demand. Once validated through a wide range of experimental data, the model capability was shown by carrying out a broad sensitivity analysis assessing the performance of the BMED technology for industrial-scale applications. Results showed how the performance of a BMED unit changes with both varying process conditions and the installed membrane area. Particularly, the non-ideal phenomena that reduce the produced NaOH concentration and increase the energy consumption were thoroughly investigated. Finally, this study demonstrated that a Levelized Cost Of Caustic Soda of about 280 € ton-1NaOH can be obtained, thus making this technology a possible candidate for the industrial production of caustic soda from brines in the future
Performance and Perspectives of an Acid/Base Flow Battery
Full text linkRecently, the utilisation of renewable energy sources is a matter of increasing importance in Europe for Energy Transition and to achieve energy independence. To this aim, tailored Electric Energy Storage (EES) devices must be employed to tackle the issue of fluctuating production from renewables. The Acid/Base Flow Battery (AB-FB) is a cutting-edge technology that allows energy to be stored in the form of acidic and alkaline solutions (van Egmond et al., 2018). This method employs two membrane processes, one for the charge phase and one for the discharge phase, namely Electrodialysis with Bipolar Membrane (EDBM) and Reverse Electrodialysis with Bipolar Membrane (REDBM), respectively. The polymeric membranes and the two electrodes are the main components of this battery. The AB-FB is a novel technology, and a lot of effort is needed to properly assess its current and future potential and identify the geometrical and operating conditions maximising its performance. This study presents a techno-economic analysis (TEA) carried out by using technically optimal results from a previous bi-objective optimisation (Culcasi, et al., 2022b). By assessing the sensitivity on the input parameters, the Levelized Cost of Storage (LCOS) of a battery operating in closed-loop and using current commercial membranes spanned from 0.17 € kWh−1 to 0.45 € kWh−1, indicating that the AB-FB has significant potential in the commercial market
Producing Hydrogen and Fresh Water from Brackish Water Desalination via Electrodialysis
Full text linkNowadays scientists and communities are particularly worried about climate change and the transition towards renewable energies is a matter of crucial importance. In this context, European Community is strongly promoting the production of green hydrogen, i.e. the electrolysis of water coupled with renewable electrical sources. In addition to the energy issue, United Nations have identified the lack of water as another big issue for modern society. Electrodialysis (ED) is an electro-membrane process able to desalinate a salty feed by the application of an external power supply. It takes advantage of the use of ionic exchange membranes which allow a controlled separation of ions from the salty feed to obtain freshwater and brine as outputs.
The present work tries to address the above issues by proposing the simultaneous production of fresh water and hydrogen with an integrated hydrogen-electrodialysis (HED) unit. The aim of the present work is that of assessing the economic feasibility of the process. To this purpose, a techno-economic model has been developed to predict the behaviour of the HED unit. The model is able to predict experimental data and should be regarded as a simple yet reliable tool to assess the process economic feasibility. Preliminary analyses suggest that the simultaneous production of hydrogen and fresh water may be profitable
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Computational Fluid Dynamics and Population Balance Model Enhances the Smart Manufacturing and Performance Optimization of an Innovative Precipitation Reactor
Full text linkIn this study, we propose the study of an innovative precipitation prototype designed by ResourSEAs, guided by a CFD-PBM (Computational Fluid Dynamics and Population Balance Model) approach, aiming to understand the influence of reactant concentration and nozzle orientation on precipitation processes. The first part of the study examines the effect of reactant concentration on supersaturation and the zeroth-order moment (m0) within a controlled flow and turbulence fields. Three different concentrations of Mg2+ (0.1, 0.3, and 0.6 M) and OH− (0.005, 0.01, and 0.02 M) were tested, resulting in varying supersaturation profiles and m0 fields. Our results show that, under equal turbulence conditions, increasing the concentration of reactants beyond a certain point actually slows down mixing, which in turn hinders the generation of supersaturation. As a result, supersaturation profiles become nearly identical to those of lower concentrations, despite having consumed more reactants. The second part of this study focuses on the effect of nozzle orientation and positioning along the prototype axis on reactant mixing and particle formation. The simulations reveal that nozzle orientation has a significant impact on the formation of primary particles, especially when positioned in low-velocity regions, leading to slower mixing and greater particle growth. Conversely, high-velocity regions promote faster mixing and more intense aggregation. These findings highlight the interplay between concentration, nozzle orientation, and flow conditions in determining precipitation efficiency, offering insights for optimizing reactor design in industrial applications
A comprehensive multi-scale process model of bipolar membrane electrodialysis (BMED) systems
Full text linkBipolar membrane electrodialysis (BMED) uses electrical energy to produce acidic and alkaline solutions by water dissociation. Its great versatility has increasingly gained the interest in chemical/biochemical industry and in environmental protection. Co-ion leakages through the membranes and shunt currents pose major issues leading to significant drops in current efficiency.
This work focuses on the development of a novel model based on a multi-scale approach. Four different dimensional scales were fully integrated within a comprehensive simulating tool with distributed parameters. The lowest scale, which is represented by the channel, includes two sub-models. The CFD simulations sub-level estimates polarization phenomena and pressure losses, while the other sub-level calculates the physical properties of the solutions. The middle-low level simulates the triplet, i.e. the repetitive unit of the stack, by computing mass balances, membrane fluxes, electrical resistance and electromotive force. The middle-high scale, represented by the stack model, is made up of two sub-levels: one is intended to compute the shunt currents through the manifolds, the other one aims at calculating pressure losses in the whole stack. Finally, the highest level simulates the external hydraulic circuit accounting for external pressure losses and dynamic mass balances in the tanks.
The model was experimentally validated with both an original campaign and literature data, showing a good agreement. A sensitivity analysis was performed in order to assess the behavior of BMED systems. The process performance was evaluated by comparing current efficiency and power consumption in different scenarios. The outcome of the analysis illustrates the influence of operating variables (e.g. current density and mean flow velocity) and of the system geometry. Results highlight the key role of the manifolds features on the process efficiency
Alarms Early Detection in Dialytic Therapies via Machine Learning Models
No full textHemodialysis (HD) is a clinical treatment for patients affected by Chronic Kidney Disease (CKD). The goal of a treatment is to purify the patient’s blood using dialysis machines, devices that act as artificial kidneys. However, a common problem is the alteration of the patient’s health status due to side effects or to machine malfunctions that may occur during treatment. A dialysis machine is a complex apparatus consisting of a control system of several quantities (e.g., pressure, flow rate, temperature, conductivity, etc.) capable of alerting medical operators when an alarm occurs. In the present work, a Machine Learning (ML) predictive model able to act in advance with respect to the dialysis alarm system was developed. Several machine learning models were tested and a comparison study was carried out. Datasets for training and testing the models came from treatments performed by dialysis machines manufactured by Mozarc Medical®. Among the models tested, the Random Forest (RF) classifier was identified as the more promising one and was then used to perform a parametric sensitivity study. By using a time window of 10 seconds, the RF model provided a Recall of 79% and an F1-Score of up to 85% on test data, demonstrating the good generalization ability that is always required by predictive models such as the one analysed in this paper
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