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Auswahl der wissenschaftlichen Literatur zum Thema „Photo-chemical process modelling“
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Zeitschriftenartikel zum Thema "Photo-chemical process modelling"
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Segarra-Martí, Javier, Sara M. Nouri und Michael J. Bearpark. „Modelling Photoionisations in Tautomeric DNA Nucleobase Derivatives 7H-Adenine and 7H-Guanine: Ultrafast Decay and Photostability“. Photochem 1, Nr. 2 (10.09.2021): 287–301. http://dx.doi.org/10.3390/photochem1020018.
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The study of radiation effects in DNA is a multidisciplinary endeavour, connecting the physical, chemical and biological sciences. Despite being mostly filtered by the ozone layer, sunlight radiation is still expected to (photo)ionise DNA in sizeable yields, triggering an electron removal process and the formation of potentially reactive cationic species. In this manuscript, photoionisation decay channels of important DNA tautomeric derivatives, 7H-adenine and 7H-guanine, are characterised with accurate CASSCF/XMS-CASPT2 theoretical methods. These simulation techniques place the onset of ionisation for 7H-adenine and 7H-guanine on average at 8.98 and 8.43 eV, in line with recorded experimental evidence when available. Cationic excited state decays are analysed next, uncovering effective barrierless deactivation routes for both species that are expected to decay to their (cationic) ground state on ultrafast timescales. Conical intersection topographies reveal that these photoionisation processes are facilitated by sloped single-path crossings, known to foster photostability, and which are predicted to enable the (VUV) photo-protection mechanisms present in these DNA tautomeric species.
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Bock, Josué, Joël Savarino und Ghislain Picard. „Air–snow exchange of nitrate: a modelling approach to investigate physicochemical processes in surface snow at Dome C, Antarctica“. Atmospheric Chemistry and Physics 16, Nr. 19 (07.10.2016): 12531–50. http://dx.doi.org/10.5194/acp-16-12531-2016.
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Abstract. Snowpack is a multiphase (photo)chemical reactor that strongly influences the air composition in polar and snow-covered regions. Snowpack plays a special role in the nitrogen cycle, as it has been shown that nitrate undergoes numerous recycling stages (including photolysis) in the snow before being permanently buried in the ice. However, the current understanding of these physicochemical processes remains very poor. Several modelling studies have attempted to reproduce (photo)chemical reactions inside snow grains, but these have relied on strong assumptions to characterise snow reactive properties, which are not well defined. Air–snow exchange processes such as adsorption, solid-state diffusion, or co-condensation also affect snow chemical composition. Here, we present a physically based model of these processes for nitrate. Using as input a 1-year-long time series of atmospheric nitrate concentration measured at Dome C, Antarctica, our model reproduces with good agreement the nitrate measurements in the surface snow. By investigating the relative importance of the main exchange processes, this study shows that, on the one hand, the combination of bulk diffusion and co-condensation allows a good reproduction of the measurements (correlation coefficient r = 0.95), with a correct amplitude and timing of summer peak concentration of nitrate in snow. During winter, nitrate concentration in surface snow is mainly driven by thermodynamic equilibrium, whilst the peak observed in summer is explained by the kinetic process of co-condensation. On the other hand, the adsorption of nitric acid on the surface of the snow grains, constrained by an already existing parameterisation for the isotherm, fails to fit the observed variations. During winter and spring, the modelled concentration of adsorbed nitrate is respectively 2.5 and 8.3-fold higher than the measured one. A strong diurnal variation driven by the temperature cycle and a peak occurring in early spring are two other major features that do not match the measurements. This study clearly demonstrates that co-condensation is the most important process to explain nitrate incorporation in snow undergoing temperature gradient metamorphism. The parameterisation developed for this process can now be used as a foundation piece in snowpack models to predict the inter-relationship between snow physical evolution and snow nitrate chemistry.
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Arashiro, Larissa T., Angelica M. Rada-Ariza, Meng Wang, Peter van der Steen und Sarina J. Ergas. „Modelling shortcut nitrogen removal from wastewater using an algal–bacterial consortium“. Water Science and Technology 75, Nr. 4 (05.12.2016): 782–92. http://dx.doi.org/10.2166/wst.2016.561.
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A shortcut nitrogen removal process was investigated for treatment of high ammonium strength wastewater using an algal–bacterial consortium in photo-sequencing batch reactors (PSBRs). In this process, algae provide oxygen for nitritation during the light period, while denitritation takes place during the dark (anoxic) period, reducing overall energy and chemical requirements. Two PSBRs were operated at different solids retention times (SRTs) and fed with a high ammonium concentration wastewater (264 mg NH4+-N L−1), with a ‘12 hour on, 12 hour off’ light cycle, and an average surface light intensity of 84 μmol m−2 s−1. High total inorganic nitrogen removal efficiencies (∼95%) and good biomass settleability (sludge volume index 53–58 mL g−1) were observed in both PSBRs. Higher biomass density was observed at higher SRT, resulting in greater light attenuation and less oxygen production. A mathematical model was developed to describe the algal–bacterial interactions, which was based on Activated Sludge Model No. 3, modified to include algal processes. Model predictions fit the experimental data well. This research also proposes an innovative holistic approach to water and energy recovery. Wastewater can be effectively treated in an anaerobic digester, generating energy from biogas, and later post-treated using an algal–bacterial PSBR, which produces biomass for additional biogas production by co-digestion.
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Velilla-Prieto, L., J. Cernicharo, M. Agúndez, J. P. Fonfría, G. Quintana-Lacaci, N. Marcelino und A. Castro-Carrizo. „IRC + 10°216 mass loss properties through the study of λ3 mm emission“. Astronomy & Astrophysics 629 (September 2019): A146. http://dx.doi.org/10.1051/0004-6361/201834717.
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Low-mass evolved stars are major contributors to interstellar medium enrichment as a consequence of the intense mass-loss process these stars experience at the end of their lives. The study of the gas in the envelopes surrounding asymptotic giant branch (AGB) stars through observations in the millimetre wavelength range provides information about the history and nature of these molecular factories. Here we present ALMA observations at subarsecond resolution, complemented with IRAM-30 m data, of several lines of SiO, SiS, and CS towards the best-studied AGB circumstellar envelope, IRC + 10°216. We aim to characterise their spatial distribution and determine their fractional abundances mainly through radiative transfer and chemical modelling. The three species display extended emission with several enhanced emission shells. CS displays the most extended distribution reaching distances up to approximately 20′′. SiS and SiO emission have similar sizes of approximately 11′′, but SiS emission is slightly more compact. We have estimated fractional abundances relative to H2, which on average are equal to f(SiO) ~10−7, f(SiS) ~10−6, and f(CS) ~10−6 up to the photo-dissociation region. The observations and analysis presented here show evidence that the circumstellar material displays clear deviations from an homogeneous spherical wind, with clumps and low density shells that may allow UV photons from the interstellar medium (ISM) to penetrate deep into the envelope, shifting the photo-dissociation radius inwards. Our chemical model predicts photo-dissociation radii compatible with those derived from the observations, although it is unable to predict abundance variations from the starting radius of the calculations (~10 R*), which may reflect the simplicity of the model. We conclude that the spatial distribution of the gas proves the episodic and variable nature of the mass loss mechanism of IRC + 10°216, on timescales of hundreds of years.
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Cüce, Hüseyin, und Duygu Özçelik. „Application of Machine Learning (ML) and Artificial Intelligence (AI)-Based Tools for Modelling and Enhancing Sustainable Optimization of the Classical/Photo-Fenton Processes for the Landfill Leachate Treatment“. Sustainability 14, Nr. 18 (08.09.2022): 11261. http://dx.doi.org/10.3390/su141811261.
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This study presents a machine learning (ML)/artificial intelligence (AI)-based perspective to reliably predict and enhance the treatment efficiency of landfill leachate by classical-Fenton (c-Fenton) and photo-Fenton (p-Fenton) processes. This experiment also sought to lower treatment costs by evaluating the impact of using different numbers of UV-c (254 nm) lamps during p-Fenton processes, as well as to develop a sustainable process design for landfill leachate. In the modeling stage, the radial basis function neural network (RBFN), the feed forward neural network (FFNN), and the support vector regression (SVR) were used and the results were evaluated in a broad scanning. Our experimental results, optimized with the help of genetic algorithm (GA), showed an increasing trend in treatment efficiency and a decreasing trend in chemical usage amounts for p-Fenton oxidation. The results indicate that both treatment techniques performed (classical and p-Fenton) within 1 h contact time showed a very high pollutant removal with a reduction in COD of approximately 60% and 80%, respectively, during the first 30 min of processing. Additionally, it was noted that the COD elimination for the c-Fenton and the p-Fenton was significantly finished in first 15 min, 52% and 73%, respectively. According to the results of the optimization model, there is an increase from 62 to 82 percent under eight UV lamps compared to seven UV lamps when considering the impact of the number of UV lamps on the treatment efficiency in p-Fenton. It has been noted that when the results are taken as a whole, the better modeling abilities of ML-based models, particularly the RBFN and the FFNN, come to the fore. From a different angle, the FFNN and the RBFNN have both shown percentile errors that are extremely close to zero when MAPE values, a percentile error measure independent of the unit of the data set, are evaluated alone. Except for two tests whose desirability levels are still around 99.99%, all experiments attained outstanding desirability levels of 100.00%. This serves as more evidence for the higher modeling performance of these ML-based approaches.
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Alimi, Oyeniyi A., Edson L. Meyer und Olufemi I. Olayiwola. „Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review“. Energies 15, Nr. 16 (17.08.2022): 5964. http://dx.doi.org/10.3390/en15165964.
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The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies.
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Casado, Cintia, José Moreno-SanSegundo, Irene De la Obra, Belén Esteban García, José Antonio Sánchez Pérez und Javier Marugán. „Mechanistic modelling of wastewater disinfection by the photo-Fenton process at circumneutral pH“. Chemical Engineering Journal 403 (Januar 2021): 126335. http://dx.doi.org/10.1016/j.cej.2020.126335.
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Csögör, Zsuzsa, Michael Herrenbauer, Iris Perner, Karsten Schmidt und Clemens Posten. „Design of a photo-bioreactor for modelling purposes“. Chemical Engineering and Processing: Process Intensification 38, Nr. 4-6 (September 1999): 517–23. http://dx.doi.org/10.1016/s0255-2701(99)00048-3.
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Villota, Natalia, Luis Miguel Camarero, Jose Maria Lomas und Mikel Legaristi. „Kinetic Modelling of Photoconversion of Phenol by a Photo-Fenton Reagent and UV“. International Journal of Chemical Reactor Engineering 12, Nr. 1 (01.01.2014): 405–16. http://dx.doi.org/10.1515/ijcre-2013-0130.
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Abstract This study analyses the oxidation of phenol using Fenton reagent in presence of ultraviolet light, enhancing the mineralization capacity of the treatment. Phenol oxidation generates reaction intermediates more toxic and refractory than phenol itself. Thus, the reaction must be carried out under harder conditions, leading these compounds to biodegradable acids that can be dumped into public sewages. Then, this paper considers a complete kinetic modelling of the phenol oxidation stages, differentiating the formation rate of species generated through ortho, meta and para pathways. The model is based on a mechanism of series reactions, wherein a first step degrades into dihydroxylated intermediates (catechol, resorcinol and hydroquinone). A second stage leads to tri-hydroxylated rings (pyrogallol) and benzoquinones (o-benzoquinone and p-benzoquinone), and both species may coexist in the reaction mixture. Next, if conditions are sufficiently oxidizing, they decompose into biodegradable organic acids (muconic, formic or acetic acids). In this mechanism, the parallel reactions, due to the presence of the metal catalyst, are considered. So, on one hand, there is a formation of metal complexes between species ortho-substituted and ferric ions from the catalyst. On the other hand, there is a precipitation of part of the initial catalyst as ferric hydroxide, due to the presence of ultraviolet light, which is determined by the operation pH. Studies have been performed on the selectivity of the oxidation in function of the reaction conditions. Then, when operating with pH = 3.0, oxidation was favoured via ortho-substituted compounds. Treatment is advisable within Pho = 100–300 mg/L, being necessary to apply molar ratios of oxidant above R = 6.0 mol H2O2/mol C6H5OH to degrade these intermediates. Moreover, kinetic constants of formation of generated species are estimated, supposing that follows an evolution comparable to first-order reactions. Analysis checks that these kinetic constants present some relation to the operating conditions. In this way, a mathematical estimation is presented, showing the dependence of the kinetics on the parameters controlling the process (pH, initial concentration of phenol, oxidant molar ratio, catalyst dosage and temperature). As a result, the behaviour of the system can be predicted within the considered range.
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Hakim, Zainab Q., Scott Archer-Nicholls, Gufran Beig, Gerd A. Folberth, Kengo Sudo, Nathan Luke Abraham, Sachin Ghude, Daven K. Henze und Alexander T. Archibald. „Evaluation of tropospheric ozone and ozone precursors in simulations from the HTAPII and CCMI model intercomparisons – a focus on the Indian subcontinent“. Atmospheric Chemistry and Physics 19, Nr. 9 (16.05.2019): 6437–58. http://dx.doi.org/10.5194/acp-19-6437-2019.
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Abstract. Here we present results from an evaluation of model simulations from the International Hemispheric Transport of Air Pollution Phase II (HTAPII) and Chemistry Climate Model Initiative (CCMI) model inter-comparison projects against a comprehensive series of ground-based, aircraft and satellite observations of ozone mixing ratios made at various locations across India. The study focuses on the recent past (observations from 2008 to 2013, models from 2009–2010) as this is most pertinent to understanding the health impacts of ozone. To our understanding this is the most comprehensive evaluation of these models' simulations of ozone across the Indian subcontinent to date. This study highlights some significant successes and challenges that the models face in representing the oxidative chemistry of the region. The multi-model range in area-weighted surface ozone over the Indian subcontinent is 37.26–56.11 ppb, whilst the population-weighted range is 41.38–57.5 ppb. When compared against surface observations from the Modelling Atmospheric Pollution and Networking (MAPAN) network of eight semi-urban monitoring sites spread across India, we find that the models tend to simulate higher ozone than that which is observed. However, observations of NOx and CO tend to be much higher than modelled mixing ratios, suggesting that the underlying emissions used in the models do not characterise these regions accurately and/or that the resolution of the models is not adequate to simulate the photo-chemical environment of these surface observations. Empirical orthogonal function (EOF) analysis is used in order to identify the extent to which the models agree with regards to the spatio-temporal distribution of the tropospheric ozone column, derived using OMI-MLS observations. We show that whilst the models agree with the spatial pattern of the first EOF of observed tropospheric ozone column, most of the models simulate a peak in the first EOF seasonal cycle represented by principle component 1, which is later than the observed peak. This suggests a widespread systematic bias in the timing of emissions or some other unknown seasonal process. In addition to evaluating modelled ozone mixing ratios, we explore modelled emissions of NOx, CO, volatile organic compounds (VOCs) and the ozone response to the emissions. We find a high degree of variation in emissions from non-anthropogenic sources (e.g. lightning NOx and biomass burning CO) between models. Total emissions of NOx and CO over India vary more between different models in the same model intercomparison project (MIP) than the same model used in different MIPs, making it impossible to diagnose whether differences in modelled ozone are due to emissions or model processes. We therefore recommend targeted experiments to pinpoint the exact causes of discrepancies between modelled and observed ozone and ozone precursors for this region. To this end, a higher density of long-term monitoring sites measuring not only ozone but also ozone precursors including speciated VOCs, located in more rural regions of the Indian subcontinent, would enable improvements in assessing the biases in models run at the resolution found in HTAPII and CCMI.
Dissertationen zum Thema "Photo-chemical process modelling"
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Ogor, Florie. „Microfabrication 3D par polymérisation multiphotonique massivement parallélisée pour des applications photoniques et biomédicales“. Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0413.
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Les structures de taille submicroniques en 3D sont utiles dans de nombreux domaines (photonique,optique, biologie...). La fabrication de telles structures est difficile. La polymérisation multiphotonique est une technique adaptée, mais les temps de fabrication actuels sont longs (une journée pour fabriquer une structure d’un mm3), rendant la production industrielle couteuse et limitant le développement de ces structures. Nous présentons notre contribution au développement et à l’optimisation d’un procédé de fabrication rapide de ces structures par polymérisation multiphotonique massivement parallélisée. Deux techniques de parallélisation sont étudiées à IMT Atlantique : une avec un élément optique diffractif, et l’autre, plus étudiée dans cette thèse, avec un modulateur spatial de lumière en configuration imagerie et une résine ultrasensible TTA (annihilation triplet-triplet), permettant d’écrire avec 1920×1080 faisceaux en parallèle. L’utilisation de multiples faisceaux d’écriture peut entraîner des effets de proximité qui limitent la résolution. Nous présentons notre simulation numérique du processus photochimique pour comprendre, prédire et corriger ces effets. Ensuite, nous présentons des améliorations effectuées, identifiées grâce aux simulations et à une meilleure compréhension du système optique. La méthode de fabrication développée permet de fabriquer des structures avec une résolution d’environ un micromètre en X,Y et de plusieurs dizaines de micromètres de hauteur sur des surfaces de l’ordre du cm2 en quelques minutes. Enfin, des exemples d’applications en biologie et en ophtalmologie, adaptés à ces performances, sont présentésSubmicron 3D structures are required in many fields (photonics, optics, biology, etc.). Fabricating such structures is difficult. Multiphoton polymerization is a suitable technique, but current fabrication times are long (one day to fabricate a mm3 structure), making industrial production costly and limiting the development of these structures. We present our contribution to the development and optimization of a massively parallelised multiphoton polymerization fabrication process for these structures. Two parallelization techniques are investigated at IMT Atlantique: one using a diffractive optical element and another, studied in this thesis, using a spatial light modulator in an imaging configuration and an ultra-sensitive TTA resist (Triplet-Triplet Annihilation), enabling writing with 1920 × 1080 beams in parallel. The use of multiple write beams can lead to resolution limiting proximity effects. We present our numerical simulation model of the photochemical process to understand, predict and correct these effects. We present possible improvements based on these simulations and the improved understanding of the optical system. The fabrication method we have developed enables us to fabricate structures with a resolution of around one micrometer in X,Y and several tens of micrometers in height on surfaces of the order of cm2 in just a few minutes. Finally, examples of applications in biology and ophthalmology, adapted to the photoplotter performance are presented
Buchteile zum Thema "Photo-chemical process modelling"
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Liu, Rui, Xiaowen Lin, Xi Chen und Antonios Armaou. „Process Modelling for Photo-Iniferter RAFT with Multiple Chain Transfer Agents“. In Computer Aided Chemical Engineering, 289–94. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-28824-1.50049-1.
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Nasr Esfahani, Kourosh, Montserrat Pérez-Moya und Moisès Graells. „Modelling and Parameter Fitting of the Dosage of Hydrogen Peroxide in a Photo-Fenton Process“. In Computer Aided Chemical Engineering, 373–78. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-95879-0.50063-1.
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