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Understanding the mechanisms of trace organic contaminant removal by high retention membrane bioreactors: a critical review

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2019年第33期26卷 34085-34100页

作者： Asif, Muhammad B. Ansari, Ashley J. Chen, Shiao-Shing Nghiem, Long D. Price, William E. Hai, Faisal I. Univ Wollongong Sch Civil Min & Environm Engn Strateg Water Infrastruct Lab Wollongong NSW 2522 Australia Natl Taipei Univ Technol Inst Environm Engn & Management Taipei 10608 Taiwan Univ Technol Sydney Sch Civil & Environm Engn Ctr Technol Water & Wastewater Sydney NSW 2007 Australia Univ Wollongong Sch Chem Strateg Water Infrastruct Lab Wollongong NSW 2522 Australia

High retention membrane bioreactors (HR-MBR) combine a high retention membrane separation process such as membrane distillation, forward osmosis, or nanofiltration with a conventional activated sludge (CAS) process. Depending on the physicochemical properties of the trace organic contaminants (TrOCs) as well as the selected high retention membrane process, HR-MBR can achieve effective removal (80-99%) of a broad spectrum of TrOCs. An in-depth assessment of the available literature on HR-MBR performance suggests that compared to CAS and conventional MBRs (using micro- or ultra-filtration membrane), aqueous phase removal of TrOCs in HR-MBR is significantly better. Conceptually, longer retention time may significantly improve TrOC biodegradation, but there are insufficient data in the literature to evaluate the extent of TrOC biodegradation improvement by HR-MBR. The accumulation of hardly biodegradable TrOCs within the bioreactor of an HR-MBR system may complicate further treatment and beneficial reuse of sludge. In addition to TrOCs, accumulation of salts gradually increases the salinity in bioreactor and can adversely affect microbial activities. Strategies to mitigate these limitations are discussed. A qualitative framework is proposed to predict the contribution of the different key mechanisms of TrOC removal (i.e., membrane retention, biodegradation, and sorption) in HR-MBR.

关键词： High retention membrane bioreactors Trace organic contaminants Removal mechanisms predictive framework Membrane distillation (MD) Forward osmosis (FO) Nanofiltration (NF)

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Deep learning for kinetics parameters identification: A novel approach for multi-variate optimization

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CHEMICAL ENGINEERING JOURNAL 2024年 489卷

作者： Raponi, Antonello Marchisio, Daniele Politecn Torino Inst Chem Engn Dept Appl Sci & Technol I-10129 Turin Italy

This work presents an innovative deep-learning approach for multi-variate optimization, focusing on the identification of Mg(OH) 2 precipitation kinetics parameters. The study employs three distinct experimental datasets, one for the Population Balance Model (PBM) fitting and two for validation. These datasets explore the impact on Particle Size Distributions (PSDs) of (i) increasing the initial reactant concentrations from 0.125 to 1 M and (ii) decreasing the flow rate from 12 to 4 m/s, both in a T-mixer, (iii) increasing the initial reactant concentration over a wider concentration range from 0.01 to 1 M in a more complex Y-mixer system. Leveraging PBM, we create a dataset to train a Neural Network (NN), referred to as the 'mirror model,' which predicts kinetics parameters based on experimental sizes. Notably, the PBM, fitted with dataset (i), excels at describing changes in flow rate (dataset (ii)) and substantial reductions in reactant concentrations in the Y-mixer (dataset (iii)), even though these conditions were not encountered during the fitting step. Key Performance Indicators (KPIs) reveal that the mirror model consistently outperforms two widely used algorithms, Conjugate Gradient (CG) and Particle Swarm Optimization (PSO), highlighting its remarkable potential for practical applications.

关键词： Machine learning Population balance model Precipitation predictive framework Optimization

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Modelling banking-hall yield for property investment

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JOURNAL OF CORPORATE REAL ESTATE 2015年第1期17卷 4-25页

作者： Tipping, Malvern Newton, Roger Anglia Ruskin Univ Dept Engn & Built Environm Built Environm Cambridge England

Purpose - This paper aims to build a predictive model for the investment yield of British banking-halls. Design/methodology/approach - Empirical data of similar lots sold at previous auctions are subjected to statistical analyses utilizing a cross-sectional research design. The independent variables analysed are taken from a previous study using the same cases. Models are built using logistic regression and ANCOVA. Findings - Logistic regression generally generates better models than ANCOVA. Adivision of Britain on a north/south divide produces the best results. Rent is as good as lot size and price in modelling, but has greater utility, because it is known prior to auctions. Research limitations/implications - Cases analysed were restricted to lots let entirely as banking-halls. Other lots comprising premises only partially used as banking-halls might produce different results. Freehold was the only tenure tested. Practical implications - The study provides a form of predictive modelling for investors and their advisors using rent which is known in advance of any sale. Originality/value - The study makes an original contribution to the field, because it builds a predictive model for investment yields for this class of property. Further research may indicate if similar predictive models can be built for other classes of investment property.

关键词： Investment Yield Portfolio Banking-hall predictive framework Rent

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Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

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RESTORATION ECOLOGY 2019年第5期27卷 1053-1063页

作者： Barnard, David M. Germino, Matthew J. Pilliod, David S. Arkle, Robert S. Applestein, Cara Davidson, Bill E. Fisk, Matthew R. US Geol Survey Forest & Rangeland Ecosyst Sci Ctr 970 S Lusk St Boise ID 83706 USA

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer-scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.

关键词： ecological prediction ensemble modeling machine learning model comparison postfire restoration predictive framework

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Do novel ecosystems follow predictable trajectories? Testing the trophic surge hypothesis in reservoirs using fish

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ECOSPHERE 2016年第12期7卷

作者： Turgeon, Katrine Solomon, Christopher T. Nozais, Christian Gregory-Eaves, Irene McGill Univ Dept Biol 1205 Docteur Penfield Ave Montreal PQ H3A 1B1 Canada McGill Univ QCBS 1205 Docteur Penfield Ave Montreal PQ H3A 1B1 Canada Cary Inst Ecosyst Studies 2801 Sharon Turnpike Millbrook NY 12454 USA Univ Quebec Dept Biol 300 Allee Ursulines Rimouski PQ G5L 3A1 Canada

Hydroelectric reservoirs are novel ecosystems that provide a variety of important services. To manage these ecosystems and their fish populations effectively, we need to develop conceptual frameworks for predicting their short- and long-term responses. To advance this goal, we revisited and tested the "trophic surge hypothesis, TSH." The TSH has been widely cited in the literature, but has not been empirically tested across numerous reservoirs. The TSH suggests that fish populations should show a hump-shaped pattern (i.e., the non-equilibrium phase) after river impoundment. As such, we assembled 40 recruitment and 109 adult fish abundance time series from 19 species distributed across seven reservoirs from temperate and boreal regions, and applied curve fitting analyses and model selection. We found that the hump-shaped pattern was the predominant pattern across individual time series, providing moderate support for the TSH. Fish recruitment increased substantially during reservoir filling and was followed by an increase in adult fish lagging 3-4 years behind. The non-equilibrium phase was transient and lasted roughly eight years for recruits, whereas it could be much longer for adults. When time series were combined across regions and sites, the support for the TSH was weaker. However, we observed significant variability in the duration, timing, and magnitude of the surge across individual time series and found that the total flooded area was the most influential predictor to explain this variability. In conclusion, the TSH and related metrics can be a useful and general predictive framework to understand how fish populations may respond to impoundment. In particular, long-term management recommendations could be short-sighted if formulated before convincing evidence has emerged to show that the reservoir reached its new trophic equilibrium.

关键词： boreal catch per unit of effort (CPUE) fisheries hydroelectricity juveniles man-made lakes predictive framework reservoir temperate upsurge

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Life-history strategies in freshwater macroinvertebrates

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FRESHWATER BIOLOGY 2008年第9期53卷 1722-1738页

作者： Verberk, Wilco C. E. P. Siepel, Henk Esselink, Hans Radboud Univ Nijmegen Inst Wetland & Water Res Dept Anim Ecol & Ecophysiol NL-6525 ED Nijmegen Netherlands Radboud Univ Nijmegen Bargerveen Fdn NL-6525 ED Nijmegen Netherlands Alterra Green World Res Ctr Ecosyst Studies Wageningen Netherlands

1. Explaining spatial and temporal differences in species assemblages is a central aim of ecology. It requires a sound understanding of the causal mechanisms underlying the relationship of species with their environment. A species trait is widely acknowledged to be the key that links pattern and process, although the enormous variety of traits hampers generalization about which combination of traits are adaptive in a particular environment. 2. In three steps, we used species traits to match species and environment, and chose lentic freshwater ecosystems to illustrate our approach. We first identified key environmental factors and selected the species traits that enable the organism to deal with them. Secondly, we investigated how investments in these traits are related (e.g. through trade-offs). Thirdly, we outlined 13 life-history strategies, based on biological species traits, their interrelations known from life-history theory and their functional implications. 3. Species traits and environmental conditions are connected through life-history strategies, with different strategies representing different solutions to particular ecological problems. In addition, strategies may present an integrated response to the environment as they are based on many different traits and their interrelationships. The presence and abundance of (species exhibiting) different life-history strategies in a location may therefore give direct information about how a particular environment is experienced by the species present. 4. Life-history strategies can be used to (i) explain differences in species assemblages either between locations or in different periods;(ii) compare waterbodies separated by large geographical distances, which may comprise different regional species pools or span species distribution areas and (iii) reduce often very complex, biodiverse assemblages into a few meaningful, easily interpretable relationships.

关键词： adaptations functional groups predictive framework reproductive tactics species traits

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The New Neural Network Based framework for Fading Channel Prediction for 5G

The New Neural Network Based Framework for Fading Channel Pr...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： A. S. Konstantinov A. V. Pestryakov Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9781728147727

ISBN: (纸本)9781728147734

The paper presents a developed framework based on several neural networks with fine tuning for predicting CQI based on the time series of SINR values in order to select the best MCS on the eNB side, taking into account fading in the radio channel in downlink. A comparative analysis of the performance of modern architectures in solving the problem of forecasting time series and substantiating the choice of LSTM as the core of the predictive framework is performed. A comparative analysis of the learning algorithms of the architectures under consideration is performed. The choice of the model is justified and the performance evaluation of the developed framework is given. The choice of parameters of the developed predictive framework is justified.

关键词： 5G CSI CQI PMI RI CRI PTI CSI-Ageing CSI-Outdating Fading channel prediction Machine Learning (ML) Convolutional Neural Networks (CNN) Long Short Time Memory (LSTM) predictive framework SERPINB6 gene color rendering index (CRI) remedial investigation frameworks 5G mobile communication Time series modern architectures Neural network Fading channels PMM2 gene wireless channels Performance Evaluation learning algorithms fine tuning

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