Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Wastewater treatment plants are designed to eliminate pollutants and alleviate environmental pollution. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high nonlinearity and variation. This study used a novel technique, multi-agent deep reinforcement learning, to simultaneously optimize dissolved oxygen and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA driven strategy since it sacrifices environmental bene ts but has lower cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the author discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions

Directory of Water Related Courses Offered at Colleges and Universities in Arkansas as of November 1998

This publication lists the water and water-related courses at several universities and colleges in Arkansas as reported during the Fall of 1 998. It is anticipated that users of this directory will extend beyond college students, and will include professionals seeking continuing education, and professors desiring to exchange Information on courses. This directory is not an absolute source of water and water-related courses because all of the higher learning Institutions In Arkansas are not listed, and, secondly, because the definition of water and water-related varies from institution to institution. None-the-less this directory provides a very valuable and impressive reference on water resources courses. Users must remember that course offerings, titles, and content change; therefore, one must contact the department to confirm details about each course. We are very grateful to the many people, too numerous to list, who have cooperated in gathering the Information In this second edition of the directory

Transfer learning in wastewater treatment plant control design : from conventional to long short-term memory-based controllers

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively

Sustainable energy management benchmark at wastewater treatment plant

Urban wastewater effluents bring large amounts of nutrients, organic matter, and organic microcontaminants into freshwater ecosystems. Ensuring the quality of wastewater treatment (WWT) is one of the main challenges facing the management of wastewater treatment plants (WWTPs). How-ever, achievement of high-quality standards leads towards significant energy consumption: usually the more intensive WWT process requires additional energies. Energy efficiency at WWTP is actual mainstream on the current sustainable development agenda. The WWTP processes and methods can be considered from the standpoint of material and energy flows according to circular economy paradigm, which offers great possibilities to reuse waste originating from WWT in order to receive renewable energy. The correlation between energy and quality issues to evaluate WWTP efficiency is of a great scientific and practical interest. The main goal of the paper is to check the dependency between these two main issues in WWTP management\u2014WWT quality and energy efficiency\u2014and to determine possible limits of such relation. The municipal sewerage system of Ekaterinburg, Russia was studied within this paper. The total length of centralized sewerage system in Ekaterinburg is over 1500 km of pipes within two main sewerage basins: northern and southern. The methodological framework for the current research consisted of three steps: (i) WWT quality evaluation, (ii) energy efficiency evaluation, and (iii) WWTP Quality/Energy (Q/E) efficiency dependency matrix. For the purpose of research, authors investigated the 2015\u20132018 period. The results showed that the outputs correlate with the technical conditions of WWTPs and the implementation of the best available techniques (BATs): most of the northern WWTP values are referred to the green zone (good rank), while the southern WWTP values are situated generally in the orange zone (unsatisfactory rank). The proposed methodological approach for Q/E dependency of WWT process creates a strong but simple tool for managers to evaluate the current success of the operation of WWTP and progress towards circular economy practices implementation

Machine learning techniques implementation in power optimization, data processing, and bio-medical applications

The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts --Abstract, page iv

Cyber-Physical Systems for Smart Water Networks: A Review

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.acceptedVersio

International Conference on Civil Engineering,Infrastructure and Environment

UBT Annual International Conference is the 8th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: – Computer Science and Communication Engineering– Management, Business and Economics– Mechatronics, System Engineering and Robotics– Energy Efficiency Engineering– Information Systems and Security– Architecture – Spatial Planning– Civil Engineering , Infrastructure and Environment– Law– Political Science– Journalism , Media and Communication– Food Science and Technology– Pharmaceutical and Natural Sciences– Design– Psychology– Education and Development– Fashion– Music– Art and Digital Media– Dentistry– Applied Medicine– Nursing This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event. Edmond Hajrizi, President of UBTUBT – Higher Education Institutio