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Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10853-022-06906-5. Uniform flexible carbon nitride coatings have been synthesized by means of annealing of films, fabricated from soluble triazine-based polymeric precursors. The coatings exhibit fascinating electrochemical stability and drastically increase the capacitance of coated carbon cloth electrodes. Following the analogue with turbostratic carbons, typically produced by means of polymeric precursors pyrolysis, we demonstrate that annealing of dried nitrogen-rich polymeric films results in coatings, composed by nearly equal atomic quantities of carbon and nitrogen, according to elemental analysis, and exhibiting noticeable mechanical robustness. X-ray difffraction patterns and infrared spectra of the materials allow to characterize them as partially amorphous carbon nitride with presumably heptazinic structure. Annealed films exhibit extrinsic semiconducting behavior with optical bandgaps in the range from 1.71 to 1.99 eV and fairly good conductivity. The outstanding long-term electrochemical stability of annealed films makes them competitive with pyrolytic carbon, while much lower annealing temperatures allow preparation of nanocomposites with various particles. The precursor polymers were obtained by self-condensation of 2-amino-4,6-dichloro-1,3,5-triazine and condensation of cyanuric chloride with 5-aminotetrazole and 3-amino-1,2,4-triazole-5-carboxylic acid, respectively, in N,N-dimethylacetamide. The polymers contain mainly C–N skeletal bonds and can therefore be viewed as ‘‘extension’’ of typical carbon nitride precursors, like melamine or dicyandiamide, to polymeric structure. Open Access funding provided thanks to the CRUECSIC agreement with Springer Nature. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 713679 and by the Universitat Rovira i Virgili (URV), contract 2017MFP-COFUND-18. Funding for this research was provided by Ministerio de Ciencia, Innovacio´n y Universidades, the Agencia Estatal de Investigacio´n (AEI) and the European Regional Development Fund (ERDF), project RTI2018-096467- B-I00. The authors research group is recognized by the Comissionat per a Universitats i Recerca, DIUE de la Generalitat de Catalunya (2017 SGR 396), and supported by the Universitat Rovira i Virgili (2019PFR-URV-33). This paper has been possible with the support of the Universitat Rovira i Virgili (URV) and Banco Santander.

Carbon-based membranes integrated with anaerobic biodegradation are presented as a unique wastewater treatment approach to deal with dye effluents. This study explores the scope of ceramic-supported carbon membrane bioreactors (B-CSCM) and ceramic-supported graphene oxide membrane bioreactors (B-CSGOM) to decolorize azo dye mixtures (ADM) and other dyes. The mixture was prepared using an equimolar composition of monoazo Acid Orange 7, diazo Reactive Black 5, and triazo Direct Blue 71 dye aqueous solution. Afterwards, as in the ADM experiment, both compact units were investigated for their ability in the biodecolorization of Methylene Blue (MB) and Rhodamine B (RhB) dye solutions, which do not belong to the azo family. The obtained outcomes revealed that the conductive surface of the graphene oxide (GO) membrane resulted in a more efficient and higher color removal of all dye solutions than B-CSCM under a wide feed concentration and permeate flux ranges. The maximum color removal at low feed concentration (50 mg·L−1) and permeate flux (0.05 L·m−2·h−1) was 96% for ADM, 98% for MB and 94% for RhB, whereas it was 89%, 94% and 66%, respectively, for B-CSCM. This suggests that the robust, cost-effective, efficient nanostructures of B-CSGOM can successfully remove diverse azo dye solutions from wastewater better than the B-CSCM does.

Ceramic-supported graphene oxide membrane bioreactors have already shown their potential for the anaerobic decolorization of wastewater containing azo dyes. The primary goal of this investigation was to develop a mathematical model that would be able to describe the steady-state behavior of this biodegradation process. The developed model was calibrated and validated using independent experimental data sets with various dye structure, feed concentration, hydraulic retention time (HRT), and support materials on which the biofilm was grown. The calibrated and validated model was used to analyze the intrinsic mechanism of the process and the main finding was that hydrolysis is the rate limiting step. Hydrolysis rate constant is decreased with increasing the complexity of the dye structure. Support materials with high electron transfer capacity increased the biofilm activity, therefore, increased the hydrolysis rate constant. Acetate concentration, used as an external carbon source, improved the dye removal efficiency. However, acetate to dye ratio did not have a direct relation to dye removal efficiency. Higher hydraulic retention time (HRT) increased the contact time between dye molecules and biofilm and enhanced the dye removal efficiency, too. However, it is essential to impose the right balance between HRT and external carbon sources to make the process feasible. This project has been supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679 and by the Universitat Rovira i Virgili (URV), contract 2017MFP-COFUND-18. This article was possible thanks to the grant RTI2018-096467-B-I00 funded by MCIN/AEI/10.13039/ 501100011033 and “ERDF A way of making Europe”. The authors research group is recognized by the Comissionat per a Universitats i Recerca, DIUE de la Generalitat de Catalunya (2017 SGR 396), and supported by the Universitat Rovira i Virgili (2021PFR-URV-87).

This study used a novel integrated technology of ceramic supported carbon membrane(CSCM) to degrade azo dyes through an anaerobic mixed culture. The CSCM worked simultaneouslyas biofilm support, redox mediator, and nano-filter to enhance the dye decolorization efficiency. Thedecolorization of Acid Orange 7 (AO7) was initially investigated with and without microorganismsin both ceramic support (CS) and CSCM reactors. The CSCM bioreactor (B-CSCM), operated withmicroorganisms, gave a maximum decolorization of 98% using a CSCM evolved from 10% weight(wt.) of Matrimid 5218 solution. To know the influence of permeate flow, feed concentration, anddye structure on the decolorization process, different B-CSCMs for dye removal experiments werestudied over monoazo AO7, diazo Reactive Black 5 (RB5), and triazo Direct Blue 71 (DB71). Thehighest color removal, operated with 50 mg·L-1feed solution and 0.05 L·m-2·h-1of permeate flux,was 98%, 82%, and 72%, respectively, for AO7, RB5, and DB71. By increasing these parameters to100 mg·L-1and 0.1 L·m-2·h-1, the decolorization rate of dye solution still achieved 37% for AO7,30% for RB5, and 26% for DB71. In addition, the system was run for weeks without apparent loss ofactivity. These findings make evident that the combined phenomena taking place in CSCM bioreactorresult in an efficient, cost-effective, and ecofriendly azo dye decolorization method. This project has been supported by the European Union’s Horizon 2020 research andinnovation programme under the Marie Skłodowska-Curie grant agreement No. 713679 and by theUniversitat Rovira i Virgili (URV), contract 2017MFP-COFUND-18. Funding for this research was provided by Ministerio de Ciencia, Innovación y Universidades, the Agencia Estatal de Investigación(AEI) and the European Regional Development Fund (ERDF), project RTI2018-096467-B-I00. Theauthors research group is recognized by the Comissionat per a Universitats i Recerca, DIUE dela Generalitat de Catalunya (2017 SGR 396), and supported by the Universitat Rovira i Virgili(2019PFR-URV-33). We would like thank Constantífor her contribution to the bacterial communitycharacterization.

Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.

Early detection of small-magnitude faults in anaerobic digestion (AD) processes is a mandatory step for preventing serious consequence in the future. Since volatile fatty acids (VFA) accumulation is widely suggested as a process health indicator, a VFA soft-sensor was developed based on support vector machine (SVM) and used for generating the residuals by comparing real and predicted VFA. The estimated residual signal was applied to univariate statistical control charts such as cumulative sum (CUSUM) and square prediction error (SPE) to detect the faults. A principal component analysis (PCA) model was also developed for comparison with the aforementioned approach. The proposed framework showed excellent performance for detecting small-magnitude faults in the state parameters of AD processes.

The applicability of fouling indicators for real time performance assessment of UF feed pretreatment in RO seawater desalination was explored in a field study using an integrated seawater UF-RO desalination pilot plant. Fouling indictors were evaluated with respect to quantification of UF backwashability, unbackwashed fouling resistance and UF fouling rate. Feed water quality and coagulant dose demonstrated measurable impact on both UF fouling rate and effectiveness of foulant removal via UF backwash. Increased coagulant dose promoted higher rate of cake formation and in turn improved backwash efficiency. However, there was a maximum coagulant dose beyond which there was no further backwash improvement. Backwash effectiveness increased with higher backwash flux and duration, up to threshold upper limits, but declined as the filtration period increased above a threshold limit. Field tests during periods of temporally varying feed quality demonstrated that higher fouling rate (promoted by inline coagulation) resulted in more effective backwash and correspondingly lower progressive rise in post-backwash UF resistance. The study results suggest that real-time UF fouling indicators, based on UF filtration resistance metrics and backwash effectiveness, should be potentially useful for tracking UF performance and thus for deployment of UF feed-back control for optimal performance of UF feed pretreatment.

The concentration of volatile fatty acids (VFAs) is one of the most important measurements for evaluating the performance of anaerobic digestion (AD) processes. In real-time applications, VFAs can be measured by dedicated sensors, which are still currently expensive and very sensitive to harsh environmental conditions. Moreover, sensors usually have a delay that is undesirable for real-time monitoring. Due to these problems, data-driven soft sensors are very attractive alternatives. This study proposes different data-driven methods for estimating reliable VFA values. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained from the international water association (IWA) Benchmark Simulation Model No. 2 (BSM2). The organic load to the AD in BSM2 was modified to simulate the behavior of an anaerobic co-digestion process. The prediction and generalization performances of the different models were also compared. This comparison showed that the GP soft sensor is more precise than the other soft sensors. In addition, the model robustness was assessed to determine the performance of each model under different process states. It is also shown that, in addition to their robustness, GP soft sensors are easy to implement and provide useful insights into the process by providing explicit equations.

This work deals with the degradation of phenol based on the classical Fenton process, which is enhanced by the presence of chelating agents. Several iron-chelating agents such as ethylenediaminetetraacetic acid (EDTA), nitrilotriacetic acid (NTA), diethylenetriamine pentaacetic acid (DTPA), and ethylenediamine-N,N&rsquo, diacetic acid (EDDA) were explored, although particular attention was given to EDTA. The effect of the molar ligand to iron ratio, EDTA:Fe, initial pH, and temperature on the oxidation process was studied. The results demonstrate that the proposed alternative approach allows the capacity for degrading phenol to be extended from the usual acidic pH (around 3.0) to circumneutral pH range (6.5&ndash, 7.5). The overall feasibility of the process depends on the concentration of the chelating agent and the initial pH of the solution. The maximum phenol conversion, over 95%, is achieved using a 0.3 to 1 molar ratio of EDTA:Fe, stoichiometric ratio of H2O2 at an initial pH of 7.0, and a temperature of 30 &deg, C after 2 hours of reaction, whereas only 10% of phenol conversion is obtained without EDTA. However, in excess of ligand (EDTA:Fe &gt, 1), the generation of radicals seems to be strongly suppressed. Improvement of the phenol removal efficiency at neutral pH also occurs for the other chelating agents tested.

The progressive decline of using fossil sources in the industry means that alternative resources must be found to produce chemicals. Waste biomass (sewage sludge) and waste lignocellulosic resources (food, forestry, or paper industries) are ideal candidates to take over from fossil sources. Municipal sewage sludge, and especially primary sludge, has a significant proportion of cellulose in its composition. Proper treatment of this cellulose allows the production of interesting chemicals like levulinic acid that are precursors (bio-blocks or building blocks) for other organic chemical processes. Cellulose was extracted from municipal wet primary sludge and paper industry dried sludge with a commercial ionic liquid. More than 99% of the cellulose has been recovered in both cases. Extraction was followed by the bleaching of the cellulose for its purification. In the bleaching, a large part of the ash was removed (up to 70% with municipal sludge). Finally, the purified cellulose was converted in levulinic acid by catalyzed hydrothermal liquefaction. The reaction, done at 170 &deg, C and 7 bar, catalyzed by a tailored Br&oslash, nsted acidic ionic liquid produced levulinic acid and other by-products in smaller quantities. The process had a conversion of cellulose to levulinic acid of 0.25 with municipal sludge and of 0.31 with industrial sludge. These results fully justify the process but, require further study to increase the conversion of cellulose to levulinic acid.

Industrial wastewaters and their treatment are now placed at the heart of the environmental concerns that industries face. Some research work has been carried out in order to limit the impact of these wastes on the environment as well as their costs. In this study, wastewater dehydrated sludge (55% wt. water content) from the paper industry was used to recover cellulose by using tetrakis(hydroxymethyl)phosphonium chloride, [P(CH2OH)4]Cl, ionic liquid as a solvent. The ionic liquid has shown remarkable results in terms of cellulose extraction in addition to its non-volatility and lower toxicity compared to organic volatile solvents. All cellulose, based on dry sludge, was recovered from the industrial dehydrated sludge with better operation conditions. The influence of temperature and the quantity of ionic liquid was preliminary studied in order to optimise the extraction conditions.

Tetrakis (hydroxymethyl) phosponium chloride ionic liquid was evaluated to recover carbohydrates, especially cellulose, from municipal wastewater treatment plant (WWTP) primary sludge (96 % water content). Different process operative conditions were evaluated: temperature (35, 60 and 80 ℃), time (6, 13 and 20 h) and sludge weight:ionic liquid volume ratio (1 g TS of SLUDGE: 7, 12 and 17 mL). A three levels, three factors central design was applied to optimize the procedure of recovery of the cellulose. Precipitated solids were analysed to quantify the content of cellulose, hemicellulose, lignin, proteins and ashes. The ionic liquid showed good performances in the recovery of cellulose. The average amount of carbohydrates recovered using the ionic liquid method was comparable with the amount of carbohydrates measured by the Dubois method in the raw sludge. All cellulose was recovered in the precipitated solid, but also part of the protein and ashes. According to the statistical tests, two interactions were found: between temperature and sludge:IL ratio for recovery of hemicellulose; between temperature and time for recovery of proteins. Experimental design was not useful to find optimized conditions of the process. Experiments realised with central values (60 °C; 13 h; 1:12 mL) gave very good results, allowing the recovery of high quantities of cellulose with the best removal of ashes but maintaining high quantities of proteins in the precipitate. For these reasons central values can be considered optimal reaction conditions. The design of novel ionic liquids will be necessary to fully purify the cellulose in a single dissolution step.

Paper industry is a sector that generates large volumes of wastewater and sludge. There is a possibility to recover the cellulose contained in these effluents. Two tetrakis and imidazolium based commercial ionic liquids were used to recover the cellulose from industrial paper dried sludge. Both ionic liquids were able to recover the cellulose of the sludge (˜100%). The ratio sludge:ionic liquid had only a low influence on the recovery. The mass of cellulose recovered was the same for both ionic liquids. The tetrakis ionic liquid was able to clean the precipitate better, eliminating a major quantity of ashes. The imidazolium ionic liquid was able to leave a minor quantity of initial proteins in the precipitate. These encouraging results demonstrate that the recovery of cellulose from paper industry sludge is feasible with ionic liquids. But, improvement of the performances of the ionic liquids is necessary to allow a scale-up of the technology.

Sensor fault detection and isolation (SFDI) approaches, based on support vector regression (SVR) plant sensor models and self-organizing-map (SOM) analysis, were investigated for application to reverse osmosis (RO) desalination plant operation. SFDI-SVR and SFDI-SOM were assessed using operational data from a small spiral-wound RO pilot plant and synthetic faulty data generated as perturbations relative to normal plant operational data. SFDI-SVR was achieved without false negative (FN) detections for sensor deviations of ≳|10%| and FN detections of, at the most, ≲|5%|, and for sensor deviations of ≳|4%| at sensor fault detection (FD) thresholds of up to ∼|4%|. False positive (FP) detections were almost invariant, with respect to sensor FD, being ≲|5%| for sensor deviations of ≳|5%|. Corrections of faulty sensor readings were within SVR model accuracy (AARE < 1%) for SFDI-SVR and ≲|5%| for SFDI-SOM. Although SFDI-SOM has lower detection accuracy, it requires a single overall plant model (or SOM), while providing pictorial representation of plant operation and depiction of faulty operational trajectories.

The development of data-driven RO plant performance models was demonstrated using the support vector regression model building approach. Models of both steady state and unsteady state plant operation were developed based on a wide range of operational data obtained from a fully automated small spiral-wound RO pilot. Single output variable steady state plant models for flow rates and conductivities of the permeate and retentate streams were of high accuracy, with average absolute relative errors (AARE) of 0.70%–2.46%. Performance of a composite support vector regression (SVR) based model (for both streams) for flow rates and conductivities was of comparable accuracy to the single output variable models (AARE of 0.71%–2.54%). The temporal change in conductivity, as a result of transient system operation (induced by perturbation of either system pressure or flow rate), was described by SVR model, which utilizes a time forecasting approach, with performance level of less than 1% AARE for forecasting periods of 2 s to 3.5 min. The high level of performance obtained with the present modeling approach suggests that short-term performance forecasting models that are based on plant data, could be useful for advanced RO plant control algorithms, fault tolerant control and process optimization.

Imidazole and 2-chloropyridine were used as model molecules to investigate the acid hydrolysis kinetics of agrochemicals in high-temperature water in a batch reactor system at 28 MPa. Pseudo-first-order hydrolysis reaction rate constants for the acid hydrolysis of Imidazole and 2-chloropyridine were determined in the temperature ranges 200–600 and 400–575°C, respectively, at 28 MPa. Rate constants for both compounds increased with temperature with a local maximum observed at around 400°C due to changes in the physical properties of the solvent. Imidazole and 2-chloropyridine conversions of 0.95 and 0.99999 were respectively attained at the highest temperature after 30 min of reaction. © 2012 Canadian Society for Chemical Engineering

A neural network-based modeling approach with back-propagation and support vector regression algorithms was investigated as a mean of developing data-driven models for forecasting reverse osmosis (RO) plant performance and for potential use for operational diagnostics. The concept of plant “short-term memory” time-interval was introduced to capture the time-variability of plant performance since both a state of the plant model and standard time-series analyses for both flux decline and salt passage did not result in realistic predictive horizons for practical purposes. Past information of normalized permeate flux and salt passage were introduced as unique input variables along with process operating parameters to capture short-term plant performance variability. Sequential models, where the time-variation within each forecasting time-interval was also taken as input information, and marching forecasting models, where target values were predicted at fixed future times from past plant information, were developed. Models were trained, with normalized permeate flux and salt passage, for various model architectures, memory time-intervals and forecasting times using both back-propagation and support vector regression approaches. State of the plant models (without forecasting) were able to describe the relatively small permeate flux variations but were unable to capture salt passage trends (for any present time condition) since unsteady state phenomena could not be properly described without plant memory information. Forecasting of plant performance, with both sequential and marching models, yielded good predictive accuracy for short-term memory time-intervals in the range of 8–24 h for permeate flux and salt passage for forecasting times up to 24 h. Current work is ongoing to extend the approach for longer time scales and to incorporate data-driven forecasting models of RO plant into control strategies and process diagnostics.

Organic solute permeation, sorption, and rejection by reverse osmosis membranes, from aqueous solutions, were studied experimentally and via artificial neural networks (ANN)-based quantitative structure–property relations (QSPR), for a set of fifty organic compounds for polyamide and cellulose acetate membranes. Membrane solute sorption and passage for dead-end filtration model experiments were quantified based on radioactivity measurements for radiolabeled compounds in the feed, permeate and the membrane, while solute rejection was determined from a mass balance on the permeated solution volume. Artificial neural networks-based quantitative structure–property relations models were developed for the organic passage ( P ), sorbed ( M ) and rejected ( R ) fractions using the most relevant set of molecular descriptors selected from a pool of 45 molecular descriptors by means of a correlation-based feature selection method and self-organizing maps (SOM). The analysis included pre-screening with principal components analysis and SOM of the chemical domain for the study chemicals as defined by chemical descriptors to identify the applicability domain and chemical similarities. The QSPR models predicted the P and M mass fractions within the range of the standard deviations of measurements for the experimental data set of fifty compounds. Mass balance closure (requiring that M , P and R sum to unity) was satisfactory for the experimental data set of fifty compounds and for an external set of 144 test chemicals, which were not included in the model development. Somewhat higher prediction errors were encountered for a few chemicals that were not well represented within the present chemical domain. The quality of the QSPR/NN models developed suggests that there is merit in extending both the present compound database and the present approach to develop a comprehensive tool for assessing organic solute behavior in RO water treatment processes.

A detailed reaction network for the catalytic wet air oxidation (CWAO) of phenol on a CuO/-Al2O3 catalyst is proposed in this study. The reaction network proposed accounts for all detected intermediate products of phenol oxidation overcoming the usual lumping of compounds. The model is composed by several consecutive and parallel reactions. The parameters of the model were adjusted using experimental data obtained from a continuous trickle bed reactor using air as oxidant at different temperatures (120–160 ◦ C) and oxygen partial pressures (0.6–1.2 MPa). Simple power law as well as Langmuir–Hinshelwood (L–H) expressions accounting for the adsorption effects were checked in the modelling of the reaction network. A robust non-linear multiparameter estimation approach called simulated annealing was used to simultaneously evaluate the high number of model parameters (up to 38). Approach by simple power law only succeeded in fitting phenol disappearance. Instead, when L–H expressions are incorporated for the intermediate reaction steps, the model accurately describes all the experimental concentration profiles, giving mean deviations below 8%. In addition, all estimated parameters have physical meaning. In particular, activation energies mostly agree with those reported in the literature. © 2001 Elsevier Science B.V. All rights reserved.

Agrobacterium MC501 and the mixed culture composed of Agrobacterium MC501, Xanthomonas MC701, Corynebacterium sp. MC401, and Corynebacterium sp. MC402, all previously isolated from a coal mine area by an enrichment culture with dibenzothiophene (DBT), were used to study DBT sulfone desulfurization. Both cultures were able to use DBT sulfone as a sole source of sulfur for growth. This compound was metabolized to 2-hydroxybiphenyl (2PP) and sulfate. Agrobacterium MC501 and the mixed culture could also utilize a wide range of organic and inorganic sulfur compounds as aources of sulfur such as DBT, thianthrene, diphenylsulfide, thiophene-2-carboxylate, dibutilsulfide, methionine, cysteine, sulfate, and sulfite. Based on these results, the above-mentioned strains can be used to characterize and study coal desulfurization.

Four out of 187 strains, from enrichment cultures of dibenzothiophene (DBT), grew on DBT or thiophene 2-carboxylate as S sources. The four isolates, presumptively identified as Agrobacterium sp., Xanthomonas sp. and Corynebacterium spp., individually and together desulphurized DBT, producing 2-hydroxybiphenyl and sulphate.

Studies on coal-oil co-processing are one aspect of the research carried out in the field of coal liquefaction. The oil used in the process is likely to be a heavy petroleum residue due, in part, to the economic constraints of the process and, moreover, to the expected chemical compatibility. This project studies the influence of the kind of coal, vacuum residue, catalyst and temperature on the co-processing conversion and the quality of liquid products. The influence of partial pressure of hydrogen is studied. Two different Spanish lignites were co-processed with a typical commercial vacuum residue and in the presence or absence of solid catalysts. Two supported catalysts, one with 25% Fe, as oxide, and a second with 25% Fe plus 10% Mo, as oxides, were prepared. Prior to their utilization, the catalysts were sulphided

The microbial degradation of dibenzothiophene (DBT) and other organosulphur compounds such as thiophene-2-carboxylate (T2C) is of interest for the potential desulphurization of coal. The feasibility of degradation of DBT and T2C by Pseudomonas putida and other bacteria was analysed. Pseudomonas putida oxidized sulphur from DBT in the presence of yeast extract, but it did not when DBT was the sole source of carbon.

The growth and oxygen consumption of a variety of thermophilic, acidophilic bacteria in the presence of thiophene-2-carboxylate (T2C) and dibenzothiophene (DBT) have been determined. T2C was extremely toxic to the acidophiles in comparison with neutrophiles, but appeared to be degraded by a heterotrophicSulfolobus- like thermophile. DBT proved to be unstable at high temperatures, even in the absence of bacteria, and was not a substrate for the thermophiles.

The effect of the hydrogen donor capacity of the solvent — such as fuel oil, anthracene oil and mixtures of 10, 20 and 30 wt% of anthracene oil in fuel oil — on the solvolysis of a Catalan lignite was studied. Liquid yields of 50wt% (daf) obtained by solvolysis with fuel oil were increased when the hydrogen donor capacity of the fuel oil was enhanced by the addition of anthracene oil. A linear relation was found between the maximum coal conversion and the anthracene oil content of the mixture. The yield varied exponentially with reciprocal absolute temperature for each solvent mixture studied. A simple kinetic model was fitted to the data and a solid-fuel reaction model put forward.

The hydrogenation of Bergueda lignite was studied using hydrogen donor and non-hydrogen donor solvents, such as anthracene and fuel oil, respectively, under a hydrogen atmosphere, in the temperature range 400–450 °C. The presence of a reactive hydrogen atmosphere increased coal conversion. Maximum yields were reached at 425 °C for the anthracene oil. The product distribution quality improved with the use of a commercial hydrodesulphurization catalyst, which also increased the production of hydrogen sulphide. Under these conditions, a pseudo-first order kinetic model appropriately described the hydroliquefaction reaction rate in terms of pseudocomponents.

Extraction d'un lignite catalan par un solvant modele, la tetraline et un solvant industriel, l'huile anthracene. Etude du rendement en fonction de la temperature. Obtention d'un modele cinetique empirique

A continuous compact membrane bioreactor consisted of ceramic-supported graphene oxide membrane (CSGoM) was implemented for the first time for anaerobic biodecolorization of monoazo Acid Orange 7 (AO7), diazo Reactive Black 5 (RB5), and triazo Direct Blue 71 (DB71) solutions, showing excellent decolorization potential. The membrane was prepared by vacuum filtration of various graphene oxide solutions using a UF ceramic flat element. The decolorization efficiency of the CSGoM bioreactor, made from 1 mg·mL-1 of GO solution (B-CSGoM-1), was investigated for several structurally distinct azo dyes, initial feed concentrations, and permeate fluxes. Maximum color removal was achieved under low feed concentration (50 mg·L-1) and permeate flux (0.05 L·m-2·h-1), reaching 99% for AO7, 96% RB5, and 92% for DB71. At this low permeate flux, the biodecolorization was stable for all azo dye solutions irrespective of the feed concentration. In a subsequent experiment under higher feed concentration and permeate flux (100 mg·L-1 and 0.10 L·m-2·h-1), the decolorization slightly fell to 93%, 85%, and 81% for monoazo, diazo, and triazo solutions, respectively. The existence of anaerobic bacteria (Geobacter and Pseudomonas guangdongensis) in the B-CSGoM-1 biofilm confirms that they could perform efficient biodegradation of azo dye molecules in association with the graphene oxide membrane. This project has been supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska- Curie grant agreement No. 713679 and by the Universitat Rovira i Virgili (URV), contract 2017MFP-COFUND-18. This article was possible thanks to the grant RTI2018-096467-B-I00 funded by MCIN/AEI/10 .13039/501100011033 and “ERDF A way of making Europe”. The au- thors research group is recognized by the Comissionat per a Universitats i Recerca, DIUE de la Generalitat de Catalunya (2017 SGR 396), and supported by the Universitat Rovira i Virgili (2019PFR-URV-33). We would like to thank Dr. Constantí for her contribution to the bacterial community characterization.