Data on ion-exchange membrane fouling by humic acid during electrodialysis

This data paper aims to provide data on the effect of the process settings on the fouling of an electrodialysis pilot installation treating a sodium chloride solution (0.1 M and 0.2 M) in the presence of humic acid (1 g/L). This data was used by “Colloidal fouling in electrodialysis: a neural differential equations model” to construct a predictive model and provides interpretive insights into this dataset. 22 electrodialysis fouling experiments were performed where the electrical resistance over the electrodialysis stack was monitored while varying the crossflow velocity (2.0 cm/s - 3.5 cm/s) in the compartments, the current applied (1.41 A - 1.91 A) to the stack and the salt concentration in the incoming stream. The active cycle was maintained for a maximum of 1.5 h after which the polarity was reversed to remove the fouling layer. Additional data is gathered such as the temperature, pH, flow rate, conductivity, pressure in the different compartments of the electrodialysis stack. The data is processed to remove the effect of temperature fluctuations and some filtering is performed. To maximise the reuse potential of this dataset, both raw and processed data are provided along with a detailed description of the pilot installation and sensor locations. The data generated can be useful for researchers and industry working on electrodialysis fouling and the modelling thereof. The availability of conductivity and pH in all compartments is useful to investigate secondary effects of humic acid fouling such as the eventual decrease in membrane permselectivity or water splitting effects introduced by the fouling layer

Colloidal fouling in electrodialysis : a neural differential equations model

The attachment of colloids to the ion-exchange membranes in electrodialysis is an important hurdle when processing bio-based process streams. Previous research showed that fouling strongly depends on the crossflow velocity, the current and the salt concentration of the medium. Predicting the influence of these variables on the fouling rate is challenging due to the complex physics at play and optimising the process conditions to reduce fouling remains a challenge. The objective of this study is the development of a model to predict the dynamic behaviour of electrodialysis fouling under varying process settings to facilitate this optimisation. A neural differential equation is fit to experimental data of an electrodialysis pilot undergoing humic acid fouling. We show that this model can predict the fouling rate even when using a limited set of experimental data. The robustness of the model is demonstrated by a simulation study and a sensitivity analysis indicates that the crossflow velocity is the most important variable influencing the fouling rate (approximate to 40%). Both the effect of the current (approximate to 20%), the salt concentration (approximate to 13%) and their interaction effects are considerable. With the model, the evolution of the stack resistance as a result of membrane fouling can be simulated, facilitating process control or decision-making

Effect of proton pump inhibitor on microbial community, function, and kinetics in anaerobic digestion with ammonia stress

The proton pump is a convincing mechanism for ammonia inhibition in anaerobic digestion, which explained how the ammonia accumulated intercellularly due to diffusion of free ammonia. Proton pump inhibitor (PPI) was dosed for mitigating the accumulation in anaerobic digestion with ammonia stress, with respect to kinetics. Results show PPI inhibited beta-oxidation of fatty acids by targeting ATPase in anaerobic digestion with ammonia stress. Alternatively, PPI stimulated syntrophic acetate oxidization. Random forest located key genera as syntrophic consortia. Methane increased 18.72 +/- 7.39% with 20 mg/L PPI at the first peak, consistent with microbial results. The deterministic Gompertz kinetics and stochastic Gaussian processes contributed 97.63 +/- 8.93% and 2.37 +/- 8.93% in accumulated methane production, respectively. Thus, the use of PPI for anaerobic digestion allowed mitigate ammonia inhibition based on the mechanism of proton pump, facilitate intercellularly ammonia accumulation, stimulate syntrophic consortia, and eliminate uncertainty of process failure, which resulted in efficient methane production under ammonia stress

Breaking barriers with hybrid models : towards low-fouling electrodialysis for bio-based processing

Decreasing fossil fuel reserves and the environmental consequences related to the exploitation of petroleum products effectuate a shift to a more bio-based economy. This economy will be driven by biochemicals and biofuels derived from various biological feedstocks. The fermentation or direct transformation of these complex feedstocks is often preceded by a pretreatment step to activate the components of interest, followed by several downstream processing operations to isolate and purify the product from these complex feed streams. The cost of upstream and downstream processing is a major contributor to the total production cost of biochemicals. Traditional purification steps often include a combination of chemical and physical separation processes and are resource- and energy-intensive. Recent studies indicate that electrodialysis can be a cost-effective alternative to replace some of these costly upstream and downstream processing steps. This membrane separation technology makes use of an electric field to remove ions from a feed stream. Despite the advantages in terms of cost/material efficiency, one of the main bottlenecks for the application of electrodialysis on bio-based process streams is fouling. The physicochemical properties of the bio-based feedstocks lead to fouling of the membranes, spacers and clogging of the system and is a major hurdle to overcome. Several experimental studies have been performed on fouling in electrodialysis systems but have not yet led to a clear solution for the targeted bio-based streams. A limited understanding and lacking quantitative description of the complex fouling phenomena lies at the heart of this problem. Given the growing market share of the bio-based economy along with a growing pressure towards sustainability, a fouling resilient electrodialysis system, tailored to withstand the potential fouling in electrodialysis processing of bio-based process streams would be a significant improvement. Even if the increase in efficiency is modest, it will be an important contribution to the economy and the environment. Previous research shows that the fouling severity is heavily influenced by the process conditions and stack design but designing and optimising electrodialysis systems to reduce fouling is challenging. This work aims at tackling this problem by developing a mathematical model of the fouling process. A mathematical model that can predict the fouling behaviour as a function of the feed stream and foulant properties, the electrodialysis stack characteristics and the operational conditions could be leveraged as a digital twin for operational optimisation and model-based design and accelerate the innovation process. A lot of models have been developed to unravel the mechanisms of ED fouling but none of them is developed to relate the process settings to the fouling rate, defined as an amount of foulant attached to the membrane or as electrical resistance. To optimise ED performance in the presence of foulants, this formulation is essential and is the main focus of this work. The first methodological part of the thesis focuses on the available mechanistic models to simulate fouling in electrodialysis. The goal of this part is to select a suitable electrodialysis process model and analyse the available fouling models. The different modelling frameworks to simulate the electrodialysis process are summarised and the trade-off between dimensionality, generalisability and computational cost of these modelling frameworks is discussed. The Nernst-Planck framework is compared to the Kedem-Katchalsky framework and several efficient simplifications of the ion transport and the hydrodynamics are reviewed as potential process models. Different open-source software libraries are highlighted that can be used to simulate these frameworks. The next chapter proposes extensions of three prominent mechanistic models to dynamically simulate electrodialysis fouling. The extended models are calibrated with experimental data and the suitability of these mechanistic approaches is determined through a combination of scenario and identifiability analyses. It was concluded that due to the large uncertainty on the underlying physics and limited specificity of the experimental data, a data-driven approach has to be adopted to fill in the gaps of missing knowledge in these mechanistic models and simulate electrodialysis fouling. The second methodological part focuses on the data-driven modelling of fouling. A dataset is gathered of the fouling behaviour of a pilot electrodialysis installation treating a humic acid solution at different operational conditions. The evolution of the stack resistance is monitored while varying the crossflow velocity, the current density and the salt concentration. Neural differential equations are put forth as a novel data-driven modelling framework for dynamic systems and the experimental data is used to train this model. The neural differential equation framework shows accurate predictions of the fouling dynamics even when a limited set of experimental data is used for model training. The robustness of the model is demonstrated by a scenario analysis and a sensitivity analysis indicates that the crossflow velocity is the most important variable influencing the fouling rate. The third methodological part extends the neural differential equations with a mechanistic process model and a mechanistic fouling model. The previous chapters establish that the development of a mechanistic fouling model is challenged by a large uncertainty on the underlying physics. Especially the mechanisms of foulant-membrane interaction, gelation and precipitation processes seem to be poorly understood. This work shows that neural differential equations can be used to fill in these knowledge gaps and improve the accuracy of the fouling models. Furthermore, we show that by including mechanistic knowledge in the data-driven model, the need for experimental data can be reduced while improving the generalisability of the model. This concept is applied to predict polyacrylamide fouling during electrodialysis. A historic dataset is used to calibrate and validate this hybrid model where the evolution of the stack resistance is predicted in time and is a function of the operational parameters and stack characteristics. Two important findings are highlighted in this part; First, the hybrid neural differential equations can leverage the mechanistic processes and generalises for new input variables which is impossible with a purely data-driven approach. Secondly, the data-driven part of the hybrid model is analysed and provides insight into the structure of the missing physics. Finally, model-based optimisation of electrodialysis operation is explored. An upcoming operational mode is pulsed electric field electrodialysis where a fluctuating current is applied to the system. Pulsed electric fields have been proved to reduce the fouling susceptibility of the system and several fouling suppression mechanisms have been put forth in previous research. A Nernst-Planck and Kedem-Katchalsky modelling approach is adopted and the evolution of the concentration profiles at the membrane surface is simulated. This model is applied to study organic fouling and sodium dodecyl sulphate is used as a foulant. An experimental study is performed and the fouling rate is studied as a function of the pulse frequency. The effect of different pulse parameters on the boundary layer concentration of sodium dodecyl sulfate is studied along with an evaluation of the current efficiency and energy consumption. Our results show that concentration relaxation is an important contributor to the fouling suppression mechanisms of pulsed electric field operation but cannot fully explain the decrease in fouling at high-frequency pulses. The simulations illustrate the counterproductivity of low-frequency pulses and the trade-off between the current efficiency and fouling suppression. Fouling layer relaxation is put forth as an additional fouling suppression effect for future research

Enhancing mechanistic models with neural differential equations to predict electrodialysis fouling

Fouling of the ion-exchange membranes by colloidal substances present in bio-based process streams is an important hurdle for electrodialysis. The development of a model is challenged by the limited availability of experimental data and the complexity of the underlying physics. This research addresses this challenge by combining a mechanistic description of the transport processes with a machine learning model to describe the complex phenomena of colloidal aggregation and attachment to the surface of ion-exchange membranes. After validation with fouling experiments using acrylamide as colloidal foulant, it was found that this hybrid model improves the predictive power of the model while reducing the need for experimental data. An analysis of both mechanistic and machine learning models showed that the attachment probability of anion polyacrylamide (APAM) is influenced by the current density and the size of the fouling layer in a non-linear manner. An increase in current density leads to an increase in the attachment probability while the opposite holds for the size of the fouling layer. This research shows that machine learning can complement mechanistic models where fundamental knowledge is lacking or computational resources are limiting. The combination maintains the interpretability and generalisability of mechanistic models while harnessing the accuracy of machine learning