Colloidal fouling in electrodialysis: A neural differential equations model

Effect of electrodialysis on colloidal geometry and dynamics: Why my membrane stack was clogged even after a fine pretreatment?

As a long-standing problem, electrodialysis (ED) clogging is believed a consequence of colloids. However, its blocking causation and clogging mechanism have not been verified. In this study, electrodialysis was used to treat a colloidal saline solution, aiming to answer the question from the "nature" of ED by investigating the influence of ED parameters such as laminar flow, salt concentration, current density and pH on colloid geometry and dynamics during the desalting process. The results revealed that: (i) laminar and membrane electrostatic repulsion and adsorption could not significantly increase the particle size (maximum 2.28 times), while the applied electric field elevated the particle size by 54.52 times (119.9 ± 13.66 to 6537.5 ± 64.35 nm); (ii) when the initial feed concentration elevated 10 times (0.1 to 1 mol/L NaCl), the particle size upsurged 149-fold (5.99 ± 0.57 to >150 μm), and flocs were generated. This enhancement was mainly attributed to the compressive electric double layer effect, and the Debye length was trimmed from 0.96 to 0.30 nm; (iii) The low current density (25 A/m2) had a profound aggregation effect on small BSA particles (roughly 10 nm); (iv) The change of pH causes the conformational transition of BSA. In the strong acidic (pH = 3.0) environment, the colloidal particle size expanded by 13 times. This study confirmed that the aggregation of colloids was the culprit of spacer clogging during electrodialysis at higher salt concentrations (>1 mol/L). Furthermore, experimental data were substituted into the simulation formula to summarise the geometry and dynamic variation of BSA in ED.

Cadmium-treatment efficiency and membrane fouling during electrodialysis of wastewater discharged from zinc smelting

Zinc smelting wastewater contains high concentrations of Cd. Here, the treatment efficiency of Cd using electrodialysis was evaluated. In addition, scale accumulation of ion-exchange membrane (IEM) was analyzed, and fouling control was studied. The results showed that spacers effectively improved the limiting current density but accelerated foulant accumulation. The Cd-treatment efficiency improved to 85.4% without a spacer. Dissolved organic carbon (DOC) and hydrophobic DOC levels in diluted water decreased by 0.65 mg L^-1 and 2.1 mg L^-1, respectively; in contrast, hydrophilic DOC level increased by 1.45 mg L^-1. Some of the hydrophobic DOC in the diluted water was converted to hydrophilic DOC and subsequently to low-molecular-weight (LMW) DOC. DOC level in the concentrated water did not change substantially, but the LMW fraction of the hydrophilic DOC increased. In the cation-exchange membrane, a material composed of calcium sulfate accumulated in the bottom layer, and hydroxides of divalent and trivalent ions accumulated on top of it. In contrast, the anion-exchange membrane was fouled by humic substances. In terms of fouling control, physical and acid cleaning of IEMs was more effective than the reversal operation.

Electrochemical Methods for Water Purification, Ion Separations, and Energy Conversion

Agricultural development, extensive industrialization, and rapid growth of the global population have inadvertently been accompanied by environmental pollution. Water pollution is exacerbated by the decreasing ability of traditional treatment methods to comply with tightening environmental standards. This review provides a comprehensive description of the principles and applications of electrochemical methods for water purification, ion separations, and energy conversion. Electrochemical methods have attractive features such as compact size, chemical selectivity, broad applicability, and reduced generation of secondary waste. Perhaps the greatest advantage of electrochemical methods, however, is that they remove contaminants directly from the water, while other technologies extract the water from the contaminants, which enables efficient removal of trace pollutants. The review begins with an overview of conventional electrochemical methods, which drive chemical or physical transformations via Faradaic reactions at electrodes, and proceeds to a detailed examination of the two primary mechanisms by which contaminants are separated in nondestructive electrochemical processes, namely electrokinetics and electrosorption. In these sections, special attention is given to emerging methods, such as shock electrodialysis and Faradaic electrosorption. Given the importance of generating clean, renewable energy, which may sometimes be combined with water purification, the review also discusses inverse methods of electrochemical energy conversion based on reverse electrosorption, electrowetting, and electrokinetic phenomena. The review concludes with a discussion of technology comparisons, remaining challenges, and potential innovations for the field such as process intensification and technoeconomic optimization.

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.

Mathematical Modeling of the Effect of Pulsed Electric Field Mode and Solution Flow Rate on Protein Fouling during Bipolar Membrane Electroacidificaiton of Caseinate Solution

A one-dimensional non-stationary model was developed for a better understanding of the protein fouling formation mechanism during electroacidification of caseinate solution using electrodialysis with bipolar membranes (EDBM) in pulsed electric field (PEF) mode. Four different PEF modes were investigated with pulse–pause durations of 10 s–10 s, 10 s–20 s, 10 s–33 s, 10 s–50 s. For each current mode 3 different flow rates were considered, corresponding to Reynolds numbers, Re, equal to 187, 374 and 560. The processes are considered in the diffusion boundary layer between the surface of the cation-exchange layer of bipolar membrane and bulk solution of the desalination compartment. The Nernst–Planck and material balance equation systems describe the ion transport. The electroneutrality condition and equilibrium chemical reactions are taken into account. The calculation results using the developed model are in qualitative agreement with the experimental data obtained during the previous experimental part of the study. It is confirmed that both the electrical PEF mode and the flow rate have a significant effect on the thickness (and mass) of the protein fouling during EDBM. Moreover, the choice of the electric current mode has the main impact on the fouling formation rate; an increase in the PEF pause duration leads to a decrease in the amount of fouling. It was shown that an increase in the PEF pause duration from 10 s to 50 s, in combination with an increase in Reynolds number (the flow rate) from 187 to 560, makes it possible to reduce synergistically the mass of protein deposits from 6 to 1.3 mg/cm2, which corresponds to a 78% decrease.

Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty

Chemical process engineering and machine learning are merging rapidly, and hybrid process models have shown promising results in process analysis and process design. However, uncertainties in first-principles process models have an adverse effect on extrapolations and inferences based on hybrid process models. Parameter sensitivities are an essential tool to understand better the underlying uncertainty propagation and hybrid system identification challenges. Still, standard parameter sensitivity concepts may fail to address comprehensive parameter uncertainty problems, i.e., deep uncertainty with aleatoric and epistemic contributions. This work shows a highly effective and reproducible sampling strategy to calculate simulation uncertainties and global parameter sensitivities for hybrid process models under deep uncertainty. We demonstrate the workflow with two electrochemical synthesis simulation studies, including the synthesis of furfuryl alcohol and 4-aminophenol. Compared with Monte Carlo reference simulations, the CPU-time was significantly reduced. The general findings of the hybrid model sensitivity studies under deep uncertainty are twofold. First, epistemic uncertainty has a significant effect on uncertainty analysis. Second, the predicted parameter sensitivities of the hybrid process models add value to the interpretation and analysis of the hybrid models themselves but are not suitable for predicting the real process/full first-principles process model’s sensitivities.

Quantitative recovery and regeneration of acidic ionic liquid 1-butyl-3-methylimidazolium hydrogen sulphate via industrial strategy for sustainable biomass processing

Quantitative recovery is necessary for scale-up application of acidic ionic liquids (AILs). Ultrafiltration and bipolar membrane electrodialysis (BMED) was employed for the recovery and regeneration of acidic ionic liquid 1-butyl-3-methylimidazolium hydrogen sulphate (Bmim[HSO₄]) after biomass pretreatment. Ultrafiltration was designed for the purification of BMED feed solution. During BMED treatment, Bmim⁺ retention with OH^- generation occurred in mixing section and SO₄^2- immigration with H⁺ generation occurred in aciding section. Resulting aqueous Bmim[OH] in mixing section and H₂SO₄ in aciding section could be utilized for quantitative synthesis of Bmim[HSO₄]. Influence of BMED operating mode and major parameters including BMED feed concentration and current density of BMED module were studied in detail. The highest recovery ratio for Bmim⁺ and SO₄^2- reached 96.2% and 96.0%. And the lowest energy consumption of specific Bmim[HSO₄] recovery approached 9.0 kw∙h/kg. Insight gained from this study suggested a sustainable biomass processing methodology using AILs.

Fouling of ion exchange membranes used in the electrodialysis reversal advanced water treatment: A review

Electrodialysis self-reversal (EDR) technology has attracted in the treatment of water for domestic and industrial uses. The self-reversal consists of a frequent reversal of the direction of current between the EDR-cell electrodes to combat fouling of ion exchange membranes (IEMs). Irrespective of the EDR self-cleaning processes, the role of natural organic matter and their complexing ability with metal ions on IEMs fouling is partially understood. The objective of this review is to identify the research gaps present in the elucidation of IEM fouling routes. The common IEMs' foulants are identified, and several fouling mechanisms are briefly discussed. The effectiveness of self-cleaning mechanisms to reduce IEMs fouling is also be discussed. Dissolved organic carbon (DOC) possesses high chelation which forms metal complexes with di and trivalent cations found in water. The role of ternary complexes, e.g. M^2+/3+-DOC and membrane surface, on membrane fouling via surface bridging, are also addressed. Finally, mitigation methods of IEMs membrane fouling are also discussed.