Article
Computer Science, Artificial Intelligence
Abdelmounaim Khallouq, Asma Karama, Mohamed Abyad
Summary: This study investigates the design of an observer-based robust tracking controller using Takagi-Sugeno fuzzy modeling for a nonlinear system in an Activated Sludge Process. A fuzzy observer and controller are designed with the goal of convergence and guaranteed H-infinity performance, with design conditions reformulated as linear matrix inequalities (LMIs) problem. The proposed controller demonstrates robust and effective tracking performance in controlling dissolved oxygen and substrate concentrations in simulations of the activated sludge process.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Liang Guo, Fu Yan, Tian Li, Tao Yang, Yuqian Lu
Summary: The traditional construction of process knowledge base is non-automated and time-consuming, which requires manual work and may lead to ambiguity in knowledge representation. This paper introduces an automatic construction framework based on knowledge graph (KG), which involves steps like classification, annotation, extraction, and representation to improve the efficiency and quality of the process knowledge base.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Engineering, Environmental
Jinkun Zhao, Hongliang Dai, Zeyu Wang, Cheng Chen, Xingwei Cai, Mengyao Song, Zechong Guo, Shuai Zhang, Xingang Wang, Hongya Geng
Summary: In this study, a self-organizing fuzzy neural network combined with predictive algorithms was used to improve the modeling and control of municipal wastewater treatment process. It could identify sewage treatment plants in real-time and provide dynamic feedback to improve water quality. The integration with model predictive control further enhanced the accuracy and efficiency of the control process. This research is of great significance for improving the efficiency of sewage treatment process.
JOURNAL OF WATER PROCESS ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xu Zhang, Zhixue Liao, Lichao Ma, Jin Yao
Summary: This study develops a mathematical model for integrated process planning and scheduling (IPPS) problems and proposes a hierarchical multistrategy genetic algorithm to solve these problems. The algorithm utilizes multiple crossover and mutation operators with global or local optimization strategies to enhance search capability and maintain population diversity. Benchmark testing confirms the effectiveness of the algorithm, particularly for high-complexity IPPS problems.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Environmental Sciences
Jia Meng, Haoran Duan, Zhiguo Yuan, Min Zheng
Summary: This study found that sludge thickening can change the buffer capacity of sludge, which affects the efficiency and acid/base usage of sludge treatment.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Sadiye Kosar, Onur Isik, Busra Cicekalan, Hazal Gulhan, Seyma Cingoz, Mustafa Yoruk, Hale Ozgun, Ismail Koyuncu, Mark C. M. van Loosdrecht, Mustafa Evren Ersahin
Summary: Achieving a neutral/positive energy balance without compromising discharge standards is a key goal in wastewater treatment plants. This study explores the coupling of high-rate activated sludge (HRAS) process with aerobic granular sludge (AGS) process as an energy-efficient pre-treatment option. The results show that feeding the AGS process with a mixture of HRAS process effluent and raw municipal wastewater can increase energy recovery potential and maintain high effluent quality.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Environmental Sciences
Weijun Zhang, Tianyi Dong, Jing Ai, Qinglong Fu, Nan Zhang, Hang He, Qilin Wang, Dongsheng Wang
Summary: Chlorination disinfection can generate harmful DBPs in wastewater sludge, but controlling the pH before and after chlorination can reduce the generation of DBPs and ensure the safety of subsequent disposal of sludge.
ENVIRONMENT INTERNATIONAL
(2022)
Article
Environmental Sciences
Guanying Wang, Guanglei Qiu, Jian Wei, Zhuang Guo, Weiye Wang, Xiaoling Liu, Yonghui Song
Summary: With the development of industries, explosion accidents occur frequently during production, transportation, usage, and storage of hazardous chemicals. The activated carbon-activated sludge (AC-AS) process shows promising potential in efficiently treating wastewater with high concentrations of toxic compounds. In this study, AC and AS were used to treat wastewater from an explosion accident, resulting in increased removal efficiency and shortened treatment time compared to traditional processes. Metagenomic analysis and spectroscopy were used to explore the enhancement mechanism of AC on AS, revealing the important roles of certain bacteria and genes in pollutant degradation. This study demonstrates the universal characteristics of the AC-AS process for treating wastewater with high levels of organic matter and toxicity.
ENVIRONMENTAL RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Kristina Kokina, Linda Mezule, Kamila Gruskevica, Romans Neilands, Ksenija Golovko, Talis Juhna
Summary: Knowledge about inhibition of activated sludge process is crucial for control of the most applied biological wastewater treatment technology. The inhibition effect of rapid variations of pH in wastewater on activated sludge was investigated in laboratory-scale sequencing batch reactors (SBR). The experiment with pH 8.5 showed a low risk of inhibition of second step nitrification (conversion of nitrites to nitrates), while the experiment with pH 6.5 showed inhibition of first-step nitrification (conversion of ammonia to nitrites).
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Abdul Gaffar Shiek, V. S. Raghu Kumar Machavolu, Murali Mohan Seepana, Seshagiri Rao Ambati
Summary: This study used an activated sludge process model to achieve simultaneous removal of nitrogen and phosphorus, and after designing and implementing 8 control approaches, it was found that all of them were able to improve the performance of the wastewater treatment plant.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Environmental Sciences
Zhenyu Hang, Peipei Tong, Pian Zhao, Zhangwei He, Linjun Shao, Yanru Jia, Xiaochang C. Wang, Zhihua Li
Summary: In this study, the stringent response of activated sludge systems to stressed conditions was examined using copper ion as a stress model. The results showed that weak stress led to an increase in AHLs concentrations and a decrease in RSG intensity. Increased stress resulted in an increase in bacteria concentration in the supernatant and a decrease in respiration rates and ATP levels. Lethal stress caused a decline in respiration rates, but an increase in Rq/t. Based on these findings, a hierarchical stringent response model was proposed.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Computer Science, Information Systems
H. Anwar Basha, S. K. B. Sangeetha, S. Sasikumar, J. Arunnehru, M. Subramaniam
Summary: A video recommendation framework using collaborative filtering (CF) process is proposed, which addresses the scalability issue through a hybrid model-based approach. The CF technique's scalability problem is addressed using KL Divergence, and a clustering scheme with enhanced sqrt-cosine similarity is proposed for more accurate movie recommendation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Environmental
Morten Kam Dahl Dueholm, Maaike Besteman, Emil Juel Zeuner, Marie Riisgaard-Jensen, Morten Eneberg Nielsen, Sofie Zacho Vestergaard, Soren Heidelbach, Nicolai Sundgaard Bekker, Per Halkjaer Nielsen
Summary: A study investigated the genomic potential for exopolysaccharide biosynthesis in bacterial species typical in activated sludge (AS) systems based on genome mining and gene synteny analyses. Putative gene clusters associated with the biosynthesis of various exopolysaccharides were identified in AS bacteria. This study provides a comprehensive overview of the genome-resolved potential for these exopolysaccharides in AS bacteria and contributes to a better understanding of EPS composition in wastewater treatment systems.
Article
Engineering, Chemical
Zeeshan Ul Haq, Hafeez Ullah, Muhammad Nouman Aslam Khan, Salman Raza Naqvi, Muhammad Ahsan
Summary: The study highlights the importance of understanding the mechanism and optimization methods for hydrogen production through supercritical water gasification. By integrating four machine learning methods and genetic algorithm, the study successfully predicts the hydrogen yield and identifies the influential parameters such as temperature, moisture content, and pressure.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2022)
Review
Agricultural Engineering
Sining Zhou, Min Liu, Ben Chen, Lianpeng Sun, Hui Lu
Summary: This review provides a systematic introduction to microbubble and nanobubble aeration in the activated sludge process, highlighting their potential applications and mechanism for improving gas-liquid mass transfer. It also discusses the recent advances and effects of microbubble and nanobubble aeration in wastewater treatment.
BIORESOURCE TECHNOLOGY
(2022)
Article
Automation & Control Systems
Ricardo Maia, Jerome Mendes, Rui Araujo, Marco Silva, Urbano Nunes
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2020)
Article
Chemistry, Multidisciplinary
Jerome Mendes, Ricardo Maia, Rui Araujo, Francisco A. A. Souza
APPLIED SCIENCES-BASEL
(2020)
Article
Chemistry, Multidisciplinary
Francisco Souza, Jerome Mendes, Rui Araujo
Summary: This paper introduces the use of regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes, evaluating different regularization methods and finding that Lasso penalty has the best impact on MoLE performance, even with a small number of training samples.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics
Jorge Pereira, Jerome Mendes, Jorge S. S. Junior, Carlos Viegas, Joao Ruivo Paulo
Summary: This study conducted a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. The current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms.
Article
Computer Science, Artificial Intelligence
Jorge S. S. Junior, Joao Ruivo Paulo, Jerome Mendes, Daniela Alves, Luis Mario Ribeiro, Carlos Viegas
Summary: This paper proposes a novel approach for the automatic calibration of fire danger classes, based on the Canadian Forest Fire Weather Index System (CFFWIS) and clustering algorithms. The approach aims to identify clusters in datasets composed of elements from CFFWIS and wildfire historical records, and associate these clusters with fire danger classes.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Construction & Building Technology
Luis Laim, Jerome Mendes, Helder D. Craveiro, Aldina Santiago, Carlos Melo
Summary: In this study, an optimized structural solution for closed built-up CFS columns under compression is proposed using the particle swarm optimization algorithm and the finite element method. The findings show that parameters such as steel thickness play a significant role in determining the optimum solution for the resistance-to-weight ratio of the columns.
JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH
(2022)
Review
Mathematics
Barbara de Matos, Rodrigo Salles, Jerome Mendes, Joana R. Gouveia, Antonio J. Baptista, Pedro Moura
Summary: Humanity is facing serious water supply problems exacerbated by population growth. Water used in human activities needs to be treated to eliminate risks to human health and the environment. Wastewater Treatment Plants (WWTPs) play a crucial role in this regard but their complex processes consume significant amounts of electrical energy. Evaluating their performance through Key Performance Indicators (KPIs) offers a way to assess their efficiency and eco-efficiency. This paper provides a literature review of KPI-based methodologies for monitoring, controlling, and optimizing energy efficiency and eco-efficiency in WWTPs.
Article
Computer Science, Information Systems
Fatima Dargam, Erhard Perz, Stefan Bergmann, Ekaterina Rodionova, Pedro Sousa, Francisco Alexandre A. Souza, Tiago Matias, Juan Manuel Ortiz, Abraham Esteve-Nunez, Pau Rodenas, Patricia Zamora Bonachela
Summary: This paper describes the work carried out in the Horizon 2020 project MIDES to develop a low-energy system for producing safe drinking water. The focus is on supporting operational decisions for desalination plants using a microbial-powered approach, from process modeling and simulation to plant monitoring and automated control. The work utilizes IPSEpro for process modeling and simulation, and DataBridge with machine learning techniques for automated control.
INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY
(2023)
Article
Energy & Fuels
Jose Eduardo Tafula, Constantino Dario Justo, Pedro Moura, Jerome Mendes, Ana Soares
Summary: Given the challenges in grid expansion, access to reliable electricity, and climate and development priorities in low-income countries, microgrids and off-grid solar projects present a viable solution for rural electrification. This study proposes a spatial framework for off-grid solar energy planning, using Geographic Information System and decision-making methods, to identify optimal locations for solar microgrid projects in Mozambique. The results show that certain criteria, such as climatology, orography, and social factors, significantly influence the selection of suitable sites. Considering geographical constraints, around 49% of the study area is suitable for off-grid solar microgrids, while 51% falls into not feasible and restricted areas due to conservation and high-risk factors.
Article
Automation & Control Systems
Jorge S. S. Junior, Jerome Mendes, Francisco Souza, Cristiano Premebida
Summary: Deep learning has gained attention in regression applications, but often lacks interpretability. This paper investigates the state-of-the-art of deep fuzzy systems (DFS), which combine deep learning and fuzzy logic systems (FLS) for regression with good accuracy and interpretability. It emphasizes the importance of considering interpretability in the development of intelligent models.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Rodrigo Salles, Jerome Mendes, Rita P. Ribeiro, Joao Gama
Summary: This paper aims to identify failures in wastewater treatment plants (WWTPs) using real-time streaming data to provide preventive actions. Convolutional and Long short-term memory (LSTM) autoencoders (AEs) were used to identify faults in the dissolved oxygen sensor in WWTPs. The best performance was achieved by Convolutional-AE.
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I
(2023)
Article
Computer Science, Information Systems
Manuel Goncalves, Pedro Sousa, Jerome Mendes, Morad Danishvar, Alireza Mousavi
Summary: The paper proposes a new real-time sensitivity analysis method, fastTracker, which is capable of efficient data processing and prioritization in large-scale complex systems. Through comparisons with other techniques, it demonstrates more accuracy and faster performance.
Proceedings Paper
Computer Science, Hardware & Architecture
Tiago Matias, Filipe F. Correia, Jonas Fritzsch, Justus Bogner, Hugo S. Ferreira, Andre Restivo
SOFTWARE ARCHITECTURE (ECSA 2020)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Jorge S. S. Junior, Joao Paulo, Jerome Mendes, Daniela Alves, Luis Mario Ribeiro
2020 IEEE THIRD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND KNOWLEDGE ENGINEERING (AIKE 2020)
(2020)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
