Abstract The quality of drinking water is a pivotal global concern, with water purification plants striving to ensure the safety and potability of the water supply. Traditional methods of water quality assessment are being augmented by advanced predictive analytics, which offer the potential for more efficient and accurate monitoring. One of the main challenges is accurately predicting water potability using diverse datasets with varying quality metrics. The complexity of water quality-related data and the need for timely decision-making call for robust predictive models that can handle high-dimensional datasets. This study introduces a simulation system that employs various machine learning classifiers, including traditional algorithms such as Logistic Regression, SVM, Decision Trees, and Random Forests, as well as advanced deep learning techniques such as CNN, LSTM, BI-LSTM, CNN-BI-LSTM, GRU, and BI-GRU. Performance evaluations are conducted using ROC curves and AUC metrics, comparing the efficacy of each model in predicting water potability. The deep learning classifiers, particularly CNN, demonstrated superior performance with a perfect AUC score of 1.00. However, this suggests potential overfitting, prompting further validation. BI-LSTM and BI-GRU also yielded high AUCs, indicating their robustness in capturing sequential patterns in the data. The implications of these findings are substantial for water purification plants, suggesting that implementing deep learning models could significantly enhance the prediction of water quality and potability. By transitioning to these advanced predictive models, plants can potentially achieve more accurate, real-time water quality monitoring, leading to improved public health outcomes.
Margaret Garcia, Aaron Deslatte, Elizabeth A. Koebele
et al.
In an era of accelerating change, urban water infrastructure systems increasingly operate outside of their design conditions, putting new pressure on systems' institutional designs to weather emerging challenges. Water management institutions must therefore be designed to exhibit dynamic fitness, defined by anticipatory capacity and responsiveness. However, we do not yet understand the specific features of institutional design that enable dynamic fitness, especially in relation to the diverse biophysical characteristics of systems that such fitness is contingent upon. We advance research on dynamic fitness in the context of urban water supply systems by drawing on 35-year data sets of stressors and responses for 16 U.S. urban water utilities using archetype analysis. Here we find that institutional archetypes capable of coping with higher biophysical complexity invest in both information processing capacity and response diversity. While dynamic fitness comes at a cost, balance between information processing capacity and response diversity promotes efficiency, which can be expanded through polycentric regional institutional structures that facilitate information sharing. Lastly, careful consideration should be given to tradeoffs across levels of governance, as institutional structures that facilitate dynamic fitness at the utility level may reduce the control and flexibility of higher levels of governance.
Vahid Eghbal Akhlaghi, Reza Zandehshahvar, Pascal Van Hentenryck
This paper considers how to fuse Machine Learning (ML) and optimization to solve large-scale Supply Chain Planning (SCP) optimization problems. These problems can be formulated as MIP models which feature both integer (non-binary) and continuous variables, as well as flow balance and capacity constraints. This raises fundamental challenges for existing integrations of ML and optimization that have focused on binary MIPs and graph problems. To address these, the paper proposes PROPEL, a new framework that combines optimization with both supervised and Deep Reinforcement Learning (DRL) to reduce the size of search space significantly. PROPEL uses supervised learning, not to predict the values of all integer variables, but to identify the variables that are fixed to zero in the optimal solution, leveraging the structure of SCP applications. PROPEL includes a DRL component that selects which fixed-at-zero variables must be relaxed to improve solution quality when the supervised learning step does not produce a solution with the desired optimality tolerance. PROPEL has been applied to industrial supply chain planning optimizations with millions of variables. The computational results show dramatic improvements in solution times and quality, including a 60% reduction in primal integral and an 88% primal gap reduction, and improvement factors of up to 13.57 and 15.92, respectively.
Based on economic theories and integrated with machine learning technology, this study explores a collaborative Supply Chain Management and Financial Supply Chain Management (SCM - FSCM) model to solve issues like efficiency loss, financing constraints, and risk transmission. We combine Transaction Cost and Information Asymmetry theories and use algorithms such as random forests to process multi-dimensional data and build a data-driven, three-dimensional (cost-efficiency-risk) analysis framework. We then apply an FSCM model of "core enterprise credit empowerment plus dynamic pledge financing." We use Long Short-Term Memory (LSTM) networks for demand forecasting and clustering/regression algorithms for benefit allocation. The study also combines Game Theory and reinforcement learning to optimize the inventory-procurement mechanism and uses eXtreme Gradient Boosting (XGBoost) for credit assessment to enable rapid monetization of inventory. Verified with 20 core and 100 supporting enterprises, the results show a 30\% increase in inventory turnover, an 18\%-22\% decrease in SME financing costs, a stable order fulfillment rate above 95\%, and excellent model performance (demand forecasting error <= 8\%, credit assessment accuracy >= 90\%). This SCM-FSCM model effectively reduces operating costs, alleviates financing constraints, and supports high-quality supply chain development.
Abstract The discharge of industrial wastewater, particularly from chemical and mining industries, poses significant threats to the environment, public health, and safety due to high concentrations of pollutants leading to serious illnesses and the loss of aquatic life. It is therefore essential and urgent to devise measures for mitigating these threats. To advance the understanding of graphene membranes for Arsenic (As) removal from wastewater, this research investigates As adsorption and its relative selectivity on graphene-based materials using computational approaches. Our study employed hybrid quantum mechanical calculations for energy and geometry optimization to explore As adsorption on pristine graphene membrane surfaces in vacuum and aqueous environments. We assessed the effect of different adsorption sites on the surface which includes the top (T), bridge (B), and hollow (H) sites across the edge (E) and center (C) regions of the absorbent surface, to identify the optimal site/mode of adsorption. Our results demonstrate that the edge sites are the most effective for adsorption, exhibiting strong adsorption energies in both vacuum (− 1.98 eV) and aqueous environments (− 1.97 eV). These values are significantly higher than the adsorption energies for water on the surface, which range from − 0.25 to − 0.26 eV. Geometrical analyses confirmed the bridge edge sites as the most preferred adsorption configuration. Our findings not only advance upon existing computational approaches for designing efficient adsorbents but also provide deeper insights into the adsorption mechanisms on graphene-based materials. Unlike previous studies, which focused primarily on experimental or theoretical aspects in isolation, this work integrates computational and theoretical approaches to optimize adsorption processes at the molecular level. By investigating membrane properties for As removal, this research offers a novel pathway for developing advanced adsorbents, addressing critical challenges in environmental remediation with greater precision and efficiency. Graphical Abstract
Water supply for domestic and industrial purposes, Environmental sciences
Phosphorus (P) is considered the leading cause of eutrophication in natural waters and has received considerable attention recently from the scientific community. In this study, P removal from aqueous solutions was investigated using bentonite, kaolinite, calcite, and zeolite mineral adsorbents modified with extract of walnut shell and wheat straw, chitosan, sodium carboxymethyl cellulose (CMC), and malic acid. Phosphorus sorption was evaluated using adsorption isotherms equations. Modified adsorbents with chitosan obtained the maximum sorption capacity of P. The results showed that P sorption capacity by Chitosan-adsorbents (bentonite (0.35 mg/g), calcite (2.09 mg/g), kaolinite (0.41 mg/g) and zeolite (0.43 mg/g)) was improved by ~ 129, 102, 128 and 119%, respectively compared to unmodified adsorbents (bentonite (0.27mg/g), calcite (2.04 mg/g), kaolinite (0.32 mg/g) and zeolite (0.36 mg/g). Langmuir and Freundlich models were used to simulate the sorption of P on modified adsorbents. The double layer model (DLM) could predict P adsorption by modified adsorbents over a wide pH range and varying ionic strength. Thermodynamic parameters showed that the nature of P adsorption by these adsorbents was non-spontaneity nature.
Environmental sciences, Water supply for domestic and industrial purposes
Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu
et al.
Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image. Diving deeper, the science stakeholder for those AI applications are Deep Generative Models, a.k.a DGMs, which are designed to learn the underlying distribution of the data and generate new data points that are statistically similar to the original dataset. One critical question is raised: how can we leverage DGMs into morden retail supply chain realm? To address this question, this paper expects to provide a comprehensive review of DGMs and discuss their existing and potential usecases in retail supply chain, by (1) providing a taxonomy and overview of state-of-the-art DGMs and their variants, (2) reviewing existing DGM applications in retail supply chain from a end-to-end view of point, and (3) discussing insights and potential directions on how DGMs can be further utilized on solving retail supply chain problems.
Abstract Using physical tools to assess online, real-time, continuous information about biofilms in technical water systems is a key component of an early-warning antifouling strategy. However, online biofilm monitoring is not yet relevant in real-field practice, nor in lab studies. In this review we analyse online biofilm monitoring from an academic perspective to provide insights on what science can improve to bring it to the spotlight of biofouling management and prevention. We argue the need to involve a broader community of biofilm researchers on the use of online monitoring to deepen biofilm studies (e.g. linking biofilm features, dynamics and operational impact) as well as the need for more, and better detailed studies. This will, consequently, reinforce the added value of biofilm monitoring as part of an early-warning antifouling strategy while bridging the techniques’ potential to the real-field needs. Finally, we propose a framework to improve laboratorial and field studies.
Considering that pontoon breakwaters are among the most common floating breakwaters, which have many advantages over other types of fixed breakwaters, therefore, in the present study, the performance of the rectangular section of this structure under the conditions of Caspian Sea waves has been studied. In this study, ANSYS AQWA software was used, and the analyzes were carried out in the form of hydrostatic analysis and time history analysis by applying the 20-year average wave conditions of the region and time history analysis under the conditions of regional limit waves. In order to validate and calibrate the model, McCartney's 1985 laboratory data has been used. The results of the research show that in the conditions of hydrostatic analysis, the highest amount of displacement occurred in the Heave movement and the lowest amount of displacement occurred in the Surge movement, which is very insignificant. The displacement in Heave movement under the wave period of 6 seconds has the highest value (1.6142 m) and the lowest value in the period of 2 seconds. Also, in the same condition, the analysis of the amount of rotation around the Z and Y axes is very small compared to the rotation around the X axis. Even the maximum values of the rotation around the Y and Z axes are less than the minimum rotation around the X axis with a value of 6.7504e-05 (°/m) which occurs in a period of 2 seconds.
In this work, the process of biogas production from palm oil factory effluent was simulated and then the produced biosynthetic gas was sweetened. For this purpose, the biogas production process from wastewater treatment was simulated using SuperPro Designer v9.0 software. Then, the resulting biogas entered the chemical absorption and reforming sections for sweetening and conversion to syngas, respectively, and these steps were simulated with Aspen HYSYS v11.0 software. The simulation results of the first stage showed that the effluent feed of this factory with a flow rate of 42000 kg/h and COD of 62000 mg/L leads to the production of 1786 kg/h biogas containing various compounds such as methane, carbon dioxide, hydrogen sulfide and water with the molar fraction of 0.446, 0.245, 0.178 and 0.040, respectively. In the chemical absorption section, MEA solvent 10 %wt. and solvent-to-gas molar ratio of 13.51 were used, which led to the efficient removal of CO2 and H2S up to 1 ppm and 99.99%, respectively. The examination of temperature changes in the absorption tower also showed that the temperature increases along the absorption tower. In the methane steam-reforming unit, two different strategies were used: 1) plug flow reactor (with fluid package of Peng-Robinson-Stryjek–Vera) and 2) conversion and equilibrium reactors (with fluid package of Peng-Robinson). The results showed that the purity of hydrogen in the biogas produced in the second strategy (conversion and equilibrium reactors) was higher than the first one (plug flow reactor), and on the other hand, the purity of CO2 was zero in the second strategy.
Technology, Water supply for domestic and industrial purposes
In the core accretion model, planetesimals grow by mutual collisions and engulfing millimeter-to-centimeter particles, i.e., pebbles. Pebble accretion can significantly increase the accretion efficiency and help explain the presence of planets on wide orbits. However, the pebble supply is typically parameterized as a coherent pebble mass flux, sometimes being constant in space and time. Here we solve the dust advection and diffusion within viciously evolving protoplanetary disks to determine the pebble supply self-consistently. The pebbles are then accreted by planetesimals interacting with the gas disk via gas drag and gravitational torque. The pebble supply is variable with space and decays with time quickly, with a pebble flux below 10 $M_\oplus$ Myr$^{-1}$ after 1 Myr in our models. As a result, only when massive planetesimals ($>$ 0.01 $M_\oplus$) are luckily produced by the streaming instability or the disk has low viscosity ($α\sim 0.0001$) can the herd of planetesimals grow over a Mars mass within 2 Myr. By then, planetesimals only capture pebbles about 50 times their mass and as little as 10 times beyond 20 au due to limited pebble supply. Further studies considering multiple dust species in various disk conditions are warranted to fully assess the realistic pebble supply and its influence on planetesimal growth.
Industrial environments are considered to be severe from the point of view of electromagnetic (EM) wave propagation. When dealing with a wide range of industrial environments and deployment setups, ray-tracing channel emulation can capture many distinctive characteristics of a propagation scenario. Ray-tracing tools often require a detailed and accurate description of the propagation scenario. Consequently, industrial environments composed of complex objects can limit the effectiveness of a ray-tracing tool and lead to computationally intensive simulations. This study analyzes the impact of using different propagation models by evaluating the number of allowed ray path interactions and digital scenario representation for an industrial environment. This study is realized using the Volcano ray-tracing tool at frequencies relevant to 5G industrial networks: 2 GHz (mid-band) and 28 GHz (high-band). This analysis can help in enhancing a ray-tracing tool that relies on a digital representation of the propagation environment to produce deterministic channel models for Indoor Factory (InF) scenarios, which can subsequently be used for industrial network design.
Abstract Groundwater is one of the most valuable natural resources on the planet, sustaining all human activity. It is necessary not just for human survival, but also for a region’s economic and social advancement. Also, agriculture and allied businesses provide a living for more than half of India’s population. Long-term advantages from sustainable agriculture will be necessary to achieve sustainable resource development and management. For successful agricultural and groundwater management, it is vital to assess the groundwater and agricultural potential of an area. This research work may contribute to optimizing the choice of location for future drilling and increase the chances to take water from productive structures which will satisfy the ever-increasing water demand of the local population, especially for agriculture. The current study is an attempt to assess the groundwater and agriculture potential zones in Haryana’s southern region of Mewat district. In order to achieve the objectives, thematic layers such as geology, geomorphology, lineament density, slope, drainage density, soil, and land use/land cover of the research region are prepared for the mapping of groundwater potential zones. For agriculture potential thematic layers such as Digital Elevation Model (DEM), Slope, Rainfall, Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Soil Moisture Index (SMI) were prepared. To combine all thematic layers, an analytical hierarchy process (AHP) assessment approach is applied. Individual themes and their accompanying categories are awarded a knowledge base weightage ranging from 1 to 5 according to their suitability to hold groundwater and potential for agriculture. All thematic maps are combined into a composite groundwater potential and agriculture potential map of the research region using the weighted overlay function. The groundwater potential map and the agriculture potential map were further subdivided into four categories, ranging from very low to excellent potential zones. It has been found that 69% and 60% of the area has moderate to good groundwater and agriculture potential, respectively, and 20% and 22% of the area has excellent and agriculture potential, respectively. This groundwater and agriculture potential information will help identify acceptable places for water extraction and efficient farming practices.
Water supply for domestic and industrial purposes, Environmental sciences
The plant-based natural coagulant has the potential to substitute the chemical coagulant in the water treatment process. In this work, the potential of plant-based natural coagulants in the ability of turbidity removal was identified. The Moringa oleifera seed was selected for the batch analysis test such as pH, contact time, agitation, and dosage. The high alkaline water decreases the effectiveness of plant-based natural coagulants. The agitation and contact time show the importance of the coagulation process. The optimum turbidity removal rate in pH is 4, the contact time is 60 seconds and 3000 seconds for coagulation and flocculation, respectively, the agitation is 300 RPM and 30 RPM for coagulation and flocculation, and lastly, the dosage is 10 g of Moringa oleifera seed. Finally, the plant-based natural coagulants demonstrated the ability to remove turbidity and could be used in place of chemical coagulants.
Abstract The aim of this paper is to understand the historical and future climate change situation using 15 extreme precipitation indices in the Pare watershed of Arunachal Pradesh, India. Historical period (1981–2019) and future period (2021–2050) precipitation data are used to compute extreme precipitation indices in RClimDex software. The Pare watershed was divided into 13 subwatersheds; however, the results of the study showed no significant spatial variation. This study found that majority of the precipitation extreme indices are showing decreasing trends during the historical period and most of them are statistically insignificant at 95% confidence level. Only three indices such as SDII, CWD and MRI are found significant at 0.05 level in the Pare watershed. Though not significant, the annual precipitation amount in the Pare watershed was found decreasing at the rate of 3.3 mm per year during the study period. The trend analysis over the whole watershed indicated significant decreasing trends for CWD and MRI while indicating significant increasing trend for SDII. The representative concentration pathway (RCP) 4.5 and 8.5 projected the extreme precipitation indices in a very similar way. The results of the trend analysis under RCP 8.5 showed significant decreasing trend only at SW10 for the index-moderate rainfall index (MRI). Various cases of RX1DAY and RX5DAY not falling during the months of monsoon were observed in both the historical and future periods. The percentage departures of the monsoon from its annual total had increased in RCP 4.5 and RCP 8.5 scenarios as compared to the historical periods. The results of this climatic investigation suggest that the precipitation regime in the study area had been accompanied and also expected by overall reduction in precipitation amount, milder rainfall events, reduction in monsoon (June–September) rainfall and drier climatic conditions. With the prevalent historical scenario and future projected scenarios of the extreme precipitation indices, the water resource potential in the study area is expected to be greatly reduced, for which the authors seek the attention of various stakeholders in water and allied sectors to come together and discuss on the construction of water conservation structures so that agricultural activities can be expanded and remain sustainable.
Abstract Hard rock aquifers of Indian peninsula are loaded with excess nitrate due to heavy use of fertilizers during irrigation and excess fluoride due to the geogenic contamination. This study is focused on the groundwater quality in Subledu Basin in view of the large-scale use of groundwater for both irrigation and drinking purposes as no such study was carried out earlier in the basin. The study area is located at Khammam district, Telangana state, India, which is a hard rock terrain mostly covered with granites and gneisses. Twenty-two groundwater samples were collected covering the entire basin in the month of May 2019 from running hand pumps for analyzing the major anions and cations in the groundwater. The samples were analyzed by using standard gravimetric method for evaluation of total dissolved solids; titrimetric methods to analyze carbonates, bicarbonates and chloride; UV spectrometric method for estimation of nitrate; and ion-selective electrode method for fluoride and spectrophotometer for sulfate and phosphate. These chemical constituents are used to calculate parameters, namely total hardness, sodium adsorption ratio, residual sodium carbonate, sodium percentage, Kelley’s ratio and magnesium hazard. The spatial distribution maps of important chemical constituents are prepared by using the contour maps created by utilizing the inverse distance weighted interpolation tool in the Geographical Information System. The excess fluoride values of 2.84 mg/l, 2.76 mg/l and 1.87 mg/l are observed in the villages of Pocharam, Kistapuram and Turakagudem, respectively, as against the maximum permissible concentration of 1.5 mg/l prescribed by World Health Organization. Excess use of fertilizers for agriculture is causing the nitrate pollution of groundwater in more than 50% of the samples with concentrations ranging from a minimum of 2 mg/l to a maximum of 460 mg/l in the villages of Medidapalle and Bachodu. It is identified that the total hardness is ranging between 200 and 820 mg/l which is very high when compared with the Bureau of Indian Standards. Based on sodium adsorption ratio, residual sodium carbonate, Kelley’s ratio, and sodium percentage analyses, two samples were not suitable for irrigation. Similarly, 13 samples are not suitable for drinking water purposes based on the excess presence of fluoride and nitrate. Groundwater quality maps of Subledu Basin depicting the areas suitable or not for the irrigation as well as for drinking purposes were prepared. From these maps, it is found that groundwater from large parts of the basin is not suitable for drinking purposes while for irrigation purposes it is suitable.
Adedapo O. Adeola, Gugu Kubheka, Evans M. N. Chirwa
et al.
Abstract The facile synthesis of graphene wool doped with oleylamine-capped silver nanoparticles (GW-αAgNP) was achieved in this study. The effect of concentration, pH, temperature and natural organic matter (NOM) on the adsorption of a human carcinogen (benzo(a)pyrene, BaP) was evaluated using the doped graphene wool adsorbent. Furthermore, the antibacterial potential of GW-αAgNP against selected drug-resistant Gram-negative and Gram-positive bacteria strains was evaluated. Isotherm data revealed that adsorption of BaP by GW-αAgNP was best described by a multilayer adsorption mechanism predicted by Freundlich model with least ERRSQ < 0.79. The doping of graphene wool with hydrophobic AgNPs coated with functional moieties significantly increased the maximum adsorption capacity of GW-αAgNP over GW based on the q max and q m predicted by Langmuir and Sips models. π-π interactions contributed to sorbent-sorbate interaction, due to the presence of delocalized electrons. GW-αAgNP-BaP interaction is a spontaneous exothermic process (negative $$\Delta H^\circ$$ Δ H ∘ and $$\Delta G)$$ Δ G ) , with better removal efficiency in the absence of natural organic matter (NOM). While GW is more feasible with higher maximum adsorption capacity (q m ) at elevated temperatures, GW-αAgNP adsorption capacity and efficiency is best at ambient temperature, in the absence of natural organic matter (NOM), and preferable in terms of energy demands and process economics. GW-αAgNP significantly inhibited the growth of Gram-negative Pseudomonas aeruginosa and Gram-positive Bacillus subtilis strains, at 1000 mg/L dosage in preliminary tests, which provides the rationale for future evaluation of this hybrid material as a smart solution to chemical and microbiological water pollution.