Wastewater Recycling to Enhance Environmental Quality using Fuzzy Embedded with RNN-IoT for Sustainable Coffee Farming DOI Open Access

Global NEST Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

<p style="text-align:justify"><span style="font-size:12pt"><span style="line-height:200%"><span style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;">Wastewater pollution is a major concern due to organic matter, pesticides, and other contaminants. Untreated discharge of this wastewater can pollute water resources harm the environment. A data-driven approach for optimizing treatment systems ensuring recycled water's safety effectiveness by calculating energy, chemical, greenhouse gas emissions. According study, process system optimization decreases negative influence on This suggested research looks at potential reusing purifying it so be used in coffee plants. variety methods cleaning disinfecting substances are detailed article. wide range physical, biological processes utilized these treatments. The primary objective sewage develop effective that ensure treated reused use agriculture. data analysis using sensors Connected measure nutrients, pollutants, salinity, pH, toxins being track various quality measures. Fuzzy-based processing utilizing FRNNs handle uncertainties inherent sensor through fuzzy logic techniques. Recurrent neural networks capture temporal dependencies data, allowing more accurate predictions. Compared with existing algorithms, proposed method has efficient its safe reuse cultivation, promoting conservation sustainable agricultural practices.</span></span></span></p>

Language: Английский

Transforming Complex Water Quality Monitoring Data into Water Quality Indices DOI Creative Commons
Nashwa A. Shaaban, David K. Stevens

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Language: Английский

Citations

2
Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review DOI Creative Commons
Voravich Ganthavee, Antoine P. Trzcinski

Environmental Chemistry Letters, Journal Year: 2024, Volume and Issue: 22(5), P. 2293 - 2318

Published: May 21, 2024

Abstract The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in context rapid industrialization urbanization. Moreover, pollutants such as antibiotics escape conventional wastewater treatments thus discharged ecosystems, requiring advanced techniques for treatment. Here we review use artificial intelligence machine learning optimize pharmaceutical treatment systems, with focus on quality, disinfection, renewable energy, biological treatment, blockchain technology, algorithms, big data, cyber-physical automated smart grid power distribution networks. Artificial allows monitoring contaminants, facilitating data analysis, diagnosing easing autonomous decision-making, predicting process parameters. We discuss advances technical reliability, energy resources management, cyber-resilience, security functionalities, robust multidimensional performance platform distributed consortium, stabilization abnormal fluctuations quality

Language: Английский

Citations

10
Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater DOI Creative Commons
Voravich Ganthavee,

Merenghege M. R. Fernando,

Antoine P. Trzcinski

et al.

Environmental Processes, Journal Year: 2024, Volume and Issue: 11(3)

Published: Aug. 1, 2024

Abstract The present study investigates the synergistic performance of three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. was treated using technique with strong oxidizing potential, and additional adsorption technology employed effectively remove pollutants Approximately 98% MO removal efficiency achieved 15 mA/cm 2 current density, 3.62 kWh/kg energy consumption 79.53% efficiency. 50 mg/L rapidly mineralized a half-life 4.66 min at density . Additionally, graphite intercalation compound (GIC) electrically polarized in reactor enhance direct electrooxidation OH generation, thereby improving treatment Decolourisation MO-polluted wastewater optimized by artificial intelligence (AI) machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), random forest (RF) algorithms. Statistical metrics indicated superiority model followed this order: ANN > RF SVM Multiple regression. optimization results parameters neural network (ANN) approaches showed that , electrolysis time 30 initial concentration were best operating maintain efficiencies reactor. Finally, Monte Carlo simulations sensitivity analysis yielded prediction lowest uncertainty variability level, whereas predictive outcome slightly better. Highlights • In-depth various techniques. Prediction 100% regeneration compound. Advanced statistical targeted responses data fitting Analysis uncertainties simulation.

Language: Английский

Citations

7
Improving Catalysts and Operating Conditions Using Machine Learning in Fischer-Tropsch Synthesis of Jet Fuels (C8-C16) DOI Creative Commons
Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano‐García

et al.

Chemical Engineering Journal Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100702 - 100702

Published: Jan. 1, 2025

Language: Английский

Citations

0
Analysis and insights of groundwater quality and water quality index values with reference to different sources: a case study DOI Open Access

B. Vamsi,

Sk. Apsar,

S. Eswar

et al.

International Journal of Engineering Science and Technology, Journal Year: 2025, Volume and Issue: 16(4), P. 11 - 19

Published: Jan. 13, 2025

Groundwater quality variation due to consequent changes in the standard of living a community is great unease owing fact that groundwater regarded as one significant water supply sources available. For sustainable use resources and management, monitoring assessment acts catalyst for an appropriate judgment on quality. In this study, samples were collected from Lingayas Institute Management Technology (LIMAT), Vijayawada campus Mudirajupalem, Krishna district, Andhra Pradesh, India assessing alkalinity, total dissolved solids (TDS), pH, acidity hardness (TH) using methods. Very high values pH TDS obtained which within vicinity agricultural fields. Added this, Student’s t test analysis signposted noteworthy P value (<0.001) mean difference was substantial statistically. The Mudirajupalem further affirmed unfit drinking, evident index (WQI) values. This study emphasizes implementing various locale specific rainwater garnering schemes, solution augmenting recharge maintaining balance.

Language: Английский

Citations

0
An Adaptive Dendritic Neural Model for Lung Cancer Prediction DOI

Umair Arif,

Chunxia Zhang,

Muhammad Waqas Chaudhary

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 3, 2025

Lung cancer is a leading cause of cancer-related deaths, often diagnosed late due to its aggressive nature. This study presents novel Adaptive Dendritic Neural Model (ADNM) enhance diagnostic accuracy in high-dimensional healthcare data. Utilizing hyperparameter optimization and activation mechanisms, ADNM improves scalability feature selection for multi-class lung prediction. Using Kaggle dataset, Particle Swarm Optimization (PSO) selected features, while bootstrap assessed performance. achieved 98.39% accuracy, 99% AUC, Cohen's kappa 96.95%, with rapid convergence via the Adam optimizer, demonstrating potential improving early diagnosis personalized treatment oncology.

Language: Английский

Citations

0
Optimized XGBoost Hyper-Parameter Tuned Model with Krill Herd Algorithm (KHA) for Accurate Drinking Water Quality Prediction DOI
Nikhil Malik,

Arpna Kalonia,

Surjeet Dalal

et al.

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(3)

Published: March 10, 2025

Language: Английский

Citations

0
Integrating machine learning and data envelopment analysis for reliable reservoir water quality index assessment considering uncertainty DOI
Mohammad Sadegh Zare, Mohammad Reza Nikoo, Ghazi Al-Rawas

et al.

Hydrological Sciences Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: March 18, 2025

Water quality assessment is crucial for environmental health and of life. This study introduces a novel water index (WQI) model reservoirs, using Wadi Dayqah Dam in Oman as case study. The advances data-driven approach, reducing reliance on subjective expert opinions. A large dataset samples was analysed machine learning (ML) to select variables (WQVs). Using bootstrapping subsampling the proposed WQI then calculated through sub-indexing, weighting, aggregating sub-indices. WQV weights were estimated gradient boosting rank order centroid techniques, while aggregation involved scoring data envelopment analysis (DEA). effectively captures uncertainty, prioritizes WQVs, provides solutions issues such eclipsing, ranking, dealing with bad variable values. results validated uncertainty sensitivity analyses, highlighting model's potential enhancing decision making reservoir management.

Language: Английский

Citations

0
Belek Özel Çevre Koruma Bölgesi Su Kalitesinin Çok Değişkenli İstatistiksel Yöntemler ile Değerlendirilmesi DOI Open Access
Ömer Faruk Özcan, Beril Salman Akın

Karadeniz Fen Bilimleri Dergisi, Journal Year: 2024, Volume and Issue: 14(2), P. 719 - 741

Published: June 18, 2024

Bu çalışmada, ülkemizde deniz kaplumbağalarının yuvalama alanı olarak koruma altında olan Belek Özel Çevre Koruma Bölgesindeki yüzey sularının uzun yıllar periyodundaki kalite değişimlerinin değerlendirilmesinde istatistiksel metotların kullanımı hedeflenmiştir. Çalışma kapsamında 2005-2020 yılları arasında (15 yıl) içinde yer alan yüzeysel su kaynaklarına ait kalitesi analiz sonuçları değerlendirilmeye alınmıştır. Yüzeysel kalitesinin sınıflandırılmasında yürürlükte Yerüstü Su Kalitesi Yönetmeliği standart değerleri çerçevesinde fiziko-kimyasal ve biyolojik parametre verileri edilmiş sınıfları belirlenmiştir. Verilerin çok değişkenli istatistiki yöntemlerden Kümeleme Analizi metodolojisi kullanılmıştır. analizi sonucunda manada anlamlı üç küme tespit edilmiştir. Kalitesine göre yapılan sınıflandırması Hiyerarşik benzerlik göstermiştir. Oluşan kümeler neticesinde genel durumunun; Acısu Deresi’nin II. Sınıf (İyi Kalite), Köprüçay I. (Çok İyi Sarısu Kömürcüler Kalite) Ilıca III. (Orta olduğu çalışmalar sonunda görülmüştür. İstatistiki değerlendirmede kullanılan Temel Bileşenler Analizine dört faktör belirlenmiş, toplam varyansın % 91,04’ünü açıklamıştır. Sadece birinci 59’unu açıklamaktadır. Özdeğeri en fazla değişkenlerin; Toplam Koliform, Kjehldal Azotu, Fekal Azot, Fosfor temel bileşenler sonuçlarına açıklanmıştır. Genel kirleticilerin turizm tesisleri, evsel kaynaklı kirleticiler yoğun tarımsal faaliyetlerden kaynaklandığı öngörülmektedir. belirlenen parametrelerin sahadaki izleme çalışmalarında öncelikli kullanılabilecek parametreler

Citations

1
An Efficient Interpretable Stacking Ensemble Model for Lung Cancer Prognosis DOI

Umair Arif,

Chunxia Zhang,

Sajid Hussain

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108248 - 108248

Published: Oct. 16, 2024

Language: Английский

Citations

1