scholarly journals Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 934
Author(s):  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Treatment Plant ◽  
Optimal Solution ◽  
Monitoring Network ◽  
Computational Effort ◽  
Bayesian Optimization ◽  
Urban Drainage ◽  
Optimization Approach ◽  
Sensor Location ◽  
Contamination Source ◽  
Monitoring Point

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

scholarly journals Exploring the application of machine learning to the assembly line feeding problem

2021 ◽  
Author(s):  
Moretti Emilio ◽  
Tappia Elena ◽  
Limère Veronique ◽  
Melacini Marco
Keyword(s):  
Machine Learning ◽  
Assembly Line ◽  
Optimal Solution ◽  
Product Variety ◽  
Supervised Machine Learning ◽  
Assembly System ◽  
Optimization Approach ◽  
Feeding Problem ◽  
Managerial Implications ◽  
Multiple Feeding

AbstractAs a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

scholarly journals Toward Decentralised Sanitary Sewage Collection Systems: A Multiobjective Approach for Cost-Effective and Resilient Designs

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1886
Author(s):  
Arezoo Zahediasl ◽  
Amin E. Bakhshipour ◽  
Ulrich Dittmer ◽  
Ali Haghighi
Keyword(s):  
Treatment Plant ◽  
Drainage System ◽  
Cost Effective ◽  
Optimization Approach ◽  
Hydraulic Design ◽  
Multiobjective Approach ◽  
Layout Generation ◽  
Plant Allocation ◽  
Problem Solving Process

In recent years, the concept of a centralized drainage system that connect an entire city to one single treatment plant is increasingly being questioned in terms of the costs, reliability, and environmental impacts. This study introduces an optimization approach based on decentralization in order to develop a cost-effective and sustainable sewage collection system. For this purpose, a new algorithm based on the growing spanning tree algorithm is developed for decentralized layout generation and treatment plant allocation. The trade-off between construction and operation costs, resilience, and the degree of centralization is a multiobjective problem that consists of two subproblems: the layout of the networks and the hydraulic design. The innovative characteristics of the proposed framework are that layout and hydraulic designs are solved simultaneously, three objectives are optimized together, and the entire problem solving process is self-adaptive. The model is then applied to a real case study. The results show that finding an optimum degree of centralization could reduce not only the network’s costs by 17.3%, but could also increase its structural resilience significantly compared to fully centralized networks.

Integrated modelling: comparison of state variables, processes and parameters in sewer and wastewater treatment plant models

1997 ◽  
Vol 36 (5) ◽  
pp. 373-380 ◽  
Author(s):  
C. Fronteau ◽  
W. Bauwens ◽  
P.A. Vanrolleghem
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Drainage System ◽  
Integrated Modelling ◽  
Urban Drainage ◽  
State Variables ◽  
Sewer System ◽  
Wastewater Treatment Process ◽  
Urban Drainage System

All the parts of an urban drainage system, i.e. the sewer system, the wastewater treatment plant (WWTP) and the river, should be integrated into one single model to assess the performance of the overall system and for the development of design and control strategies assisting in its sustainable and cost effective management. Existing models for the individual components of the system have to be merged in order to develop the integrated tool. One of the problems arising from this methodology is the incompatibility of state variables, processes and parameters used in the different modelling approaches. Optimisation of an urban drainage system, and of the wastewater treatment process in particular, requires a good knowledge of the wastewater composition. As important transformations take place between the emission from the household and the arrival at the treatment facility, sewer models should include these transformations in the sewer system. At present, however, research is still needed in order to increase our knowledge of these in-sewer processes. A comparison of the state variables, processes and parameters has been carried out in both sewer models (SMs) and activated sludge models (ASMs). An ASM approach is used for the description of reactions in sewer models. However, a difference is found in the expression for organic material (expressed in terms of BOD) and heterotrophic biomass is absent as a state variable, resulting in differences in processes and parameters. Reconciliation of both the models seems worthwhile and a preliminary solution is suggested in this paper.

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

scholarly journals Sensor Location Problem for Network Traffic Flow Derivation Based on Turning Ratios at Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Minhua Shao ◽  
Lijun Sun ◽  
Xianzhi Shao
Keyword(s):  
Network Flow ◽  
Location Problem ◽  
Optimal Solution ◽  
Coefficient Matrix ◽  
Sensor Location ◽  
The Road ◽  
Minimum Number ◽  
Flow Conservation ◽  
The Greedy Algorithm ◽  
Counting Points

The sensor location problem (SLP) discussed in this paper is to find the minimum number and optimum locations of the flow counting points in the road network so that the traffic flows over the whole network can be inferred uniquely. Flow conservation system at intersections is formulated firstly using the turning ratios as the prior information. Then the coefficient matrix of the flow conservation system is proved to be nonsingular. Based on that, the minimal number of counting points is determined to be the total number of exclusive incoming roads and dummy roads, which are added to the network to represent the trips generated on real roads. So the task of SLP model based on turning ratios is just to determine the optimal sensor locations. The following analysis in this paper shows that placing sensors on all the exclusive incoming roads and dummy roads can always generate a unique network flow vector for any network topology. After that, a detection set composed of only real roads is proven to exist from the view of feasibility in reality. Finally, considering the roads importance and cost of the sensors, a weighted SLP model is formulated to find the optimal detection set. The greedy algorithm is proven to be able to provide the optimal solution for the proposed weighted SLP model.

An optimal solution to achieve the energy neutral Waste Water Treatment Plant

2011 ◽  
Vol 6 (4) ◽  
Author(s):  
Christophe Amiel ◽  
Delphine Nawawi-Lansade ◽  
Kim Sorensen
Keyword(s):  
Energy Consumption ◽  
Municipal Wastewater ◽  
Biogas Production ◽  
Waste Water Treatment ◽  
Carbon Sources ◽  
Treatment Plant ◽  
Co2 Reduction ◽  
Optimal Solution ◽  
Waste Water Treatment Plant ◽  
Municipal Wastewater Treatment

Many recent studies have shown processes or models to minimize the energy consumption on a municipal wastewater treatment plant (WWTP) in operation. Today the main drivers are the energy and CO2 reduction. On existing plants, the potential success of achieving the Energy neutral WWTP depends on the effluent guarantees demanded and the eventual additional carbon sources on the digesters. Veolia has now developed a tool to estimate the energy consumption and the CO2 impact to select the appropriate treatment lines (water and sludge) up front at the project stage. The real challenge is to cover the needs of the plant without external carbon sources added to the digester. At the project stage, before the bid of the WWTP, due to time constraints only few comparisons can be performed to predict the energy consumption and CO2 impact and provide the best solution to reach to the energy neutral plant as electricity wise. One conclusion of the study is that, the raw water characteristics and the effluent guarantee has a great impact on the possibilities to reach the target. Furthermore, working on reducing the power consumption and on increasing the biogas production for example by a continuous Thermal hydrolysis is a good way to go towards self sufficiency.

scholarly journals A two-stage evolutionary optimization approach for an irrigation system design

2016 ◽  
Vol 19 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Milan Cisty ◽  
Zbynek Bajtek ◽  
Lubomir Celar
Keyword(s):  
Water Distribution ◽  
Irrigation System ◽  
Optimal Solution ◽  
Water Distribution Network ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

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