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ASME 18th International Conference on Energy Sustainability (ES)

作者： Bove, Giovanni Sorrentino, Marco Desideri, Umberto Univ Salerno Salerno Italy Univ Pisa Pisa Italy

ISBN: (纸本)9780791887899

The imperative of achieving net zero emission (NZE) targets is central to the global sustainability agenda, particularly in the critical sector of urban bus fleets. This study explores transitioning fleets from traditional diesel to fuel cell (FC) propulsion, identifying the most economically viable option through a comprehensive methodology, including a total cost of ownership (TCO) analysis. Despite FC technology advancements, challenges persist due to unfavorable hydrogen price and high capital expenditures, necessitating innovative alternatives. The main objective is to introduce and validate the design of a clean grid-independent hydrogen supply station, a transformative solution envisioning a fleet powered by H-2 generated from a proton exchange membrane electrolyzer (PEMEL) based renewable microgrid, overcoming challenges posed by H-2 pricing and aligning with NZE goals. The design process involves developing a tailored PEMEL-based microgrid for hydrogen production. Analyses include a simple payback (SPB) period evaluation and validation, comparing the proposed configuration with microgrid setups by other authors. Despite challenges, the grid concept associated with fleet renewal demonstrates real-world viability and economic benefits, providing a compelling case for adoption in urban bus fleet renewal. With a 40% reduction in TCO compared to FC bus renewal and a 10% reduction compared to diesel buses, it represents a sustainable and economically sound solution that significantly contributes to the global sustainability agenda.

关键词： advanced energy systems hydrogen optimization and control renewable energy

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Machine Learning prediction model for Dynamic Scheduling of Hybrid Flow-Shop based on Metaheuristic 18

Machine Learning prediction model for Dynamic Scheduling of ...

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18th IFAC Symposium on Information control Problems in Manufacturing (INCOM)

作者： Hamiti, Abdelhakim Ghiles Bouazza, Wassim Laurent, Arnaud Mebarki, Nasser Kenani, Mohamed Nantes Univ Ecole Cent Nantes CNRS LS2NUMR 6004 F-44000 Nantes France

Optimizing scheduling and allocation strategies in dynamic production environments, notably in Hybrid Flow-Shops, presents significant challenges. This study focuses on resource assignment within dynamic contexts. It proposes an approach that use Genetic Algorithm (GA) to generate data and train machine Learning (ML) to predict near optimal allocations. Through experiments across various scenarios, the accuracy of prediction of different ML models for resource allocation is evaluated. Our findings highlight the potential of ML techniques to improve decision-making in dynamic and flexible manufacturing systems (FMS), contributing to efforts to enhance reactive scheduling strategies. Future work will assess the impact of these decisions on mean completion time, providing deeper insights into on-line scheduling efficiency. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Intelligent Manufacturing Systems optimization and control Genetic Algorithms

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NONTECHNICAL BARRIERS TO BUILDING DECARBONIZATION IN THE US 18

NONTECHNICAL BARRIERS TO BUILDING DECARBONIZATION IN THE US

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ASME 18th International Conference on Energy Sustainability (ES)

作者： Osterwood, Kristen Monat, Jamie Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9780791887899

The need to reduce anthropogenic carbon emissions into the atmosphere is abundantly clear. It is also becoming widely accepted that transitioning to air source heat pumps (ASHP) is an essential part of the solution to decarbonizing buildings. The technological barriers to such a transition are covered in the literature, but the nontechnical barriers of adoption of ASHP also need to be considered. The technical focus of the decarbonization problem has blossomed into complex models as an increasing number of factors are incorporated, yielding more accurate models. Although some challenges to ASHPs are mentioned, research into the barriers and evaluation of methods to overcome those barriers are essential to successfully reduce anthropogenic carbon. Technical barriers pair with technical solutions - but nontechnical barriers are often slightly ambiguous and messy and the corresponding solutions require input from other disciplines. This research identifies the nontechnical challenges of heat pump adoption and potential solutions. First, we present a review of existing models to demonstrate the growing consensus that ASHP are an essential part of decarbonization. Second, we present the nontechnical barriers as covered in the literature and compare those with the discovered nontechnical challenges through a case study and firsthand accounts. We find that the theoretical ideas of nontechnical barriers do not all align with the barriers in reality, and that these, especially the perception of the ineffectiveness of ASHP, needs to be addressed to enable the level of adoption necessary to meet climate goals. Of the many challenges, from training to financial, the hurdle that is most pervasive, and potentially the most challenging to overcome is, perception of the ineffectiveness of heat pumps at cold temperatures. The perception of the inability of ASHP to function in cold weather is held by those from all levels of education - from the average consumer to those doing academ

关键词： energy conversion/systems energy systems power systems optimization and control carbon

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A Dispatch optimization Model for Hybrid Renewable and Battery Systems Incorporating a Battery Degradation Model

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2022年第7期144卷 070907页

作者： Upadhya, Sahana Wagner, Michael J. Univ Wisconsin Dept Mech Engn Energy Syst Optimizat Lab Madison WI 53706 USA

A recent increase in the integration of renewable energy systems in existing power grids along with a lack of integrated dispatch models has led to waste in power produced. This paper presents a mixed-integer nonlinear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Loss in capacity is penalized in the objective function of the optimization model, thereby disincentivizing high and low SOCs and frequent cycling. A rolling time horizon optimization approach is used to overcome the computational difficulties of achieving global optimality within a long-term time horizon. By incorporating battery degradation, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model within a case study using a sample photovoltaic system generation time series that considers multiple battery capacities. The results indicate that the optimal battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between battery operational decisions and the resultant capacity fade.

关键词： energy storage energy systems analysis optimization and control simulation

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Two-echelon Inventory Management for Sustainable Pharmaceutical Supply Chain through Waste Reduction 10th

Two-echelon Inventory Management for Sustainable Pharmaceuti...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and control (MIM)

作者： Romdhani, Shayma Nouaouri, Issam Tounsi, Jihene Gattoufi, Said Allaoui, Hamid Univ Tunis SMART Lab Inst Super Gest Tunis Tunis Tunisia Univ Artois UR 3926 Lab Genie Informat & Automat Artois LGI2A F-62400 Bethune France Univ Carthage IHEC Carthage Tunisia

Improving sustainability in Pharmaceutical Supply Chain (PSC) becomes the primary concern for its involved members. It lends major challenges to its management as it has economic, social, and environmental responsibilities more weighed than other supply chains. Providing the day-to-day need for medicines must be satisfied while taking into account the use of the economic resource, customer satisfaction, and the impact of pharmaceutical waste on the environment. Medicines waste affects healthcare expenses and harms the environment. Therefore, avoiding unused medication leftover through the pharmaceutical chain presents an efficient approach to attaining a sustainable supply of medicines. This article aims to deal with the sustainability of a PSC by minimizing the deterioration rate of medicines at both distributor and hospitals sites. We propose an inventory management model based on a mixed-integer non-linear program (MINLP) that seeks the optimal replenishment order quantity of multiple types of products and the shipment time in a two-echelon PSC consisting of a pharmaceutical company (PC), a central pharmacy (CP), and multiple hospitals over a planning horizon, while considering shipment costs, perishability, and shortage constraints. Copyright (C) 2022 The Authors.

关键词： optimization and control Supply Chain Management Operations Research Pharmaceutical supply chain Inventory management Sustainability Waste reduction MINLP

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control reconfiguration strategies for Remaining Useful Life extension 8

Control reconfiguration strategies for Remaining Useful Life...

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8th IFAC Symposium on Systems Structure and control (SSSC)

作者： Thuillier, J. Jha, M. S. Galeotta, M. Theilliol, D. CNES Direct Technol & Numer 52 Rue Jacques Hillairet F-75612 Paris France Univ Lorraine UMR 7039 CNRS CRAN Vandoeuvre Les Nancy France

Extending the Remaining Useful Life (RUL) of dynamic systems functioning in closed loop in accordance with damage progression dynamics is a challenging task. Such target combines the challenges emanating from the domain of Prognostic Health Management (PHM) and engineering of the control theory. The main contribution of the paper consists in the synthesis and the analysis of two control reconfiguration strategies in order to achieve such objective. This paper presents two control strategies, one reconfigures the controls input and the other reconfigures the setpoint. The first structure modifies the controls input sent to the system using a modulation parameter. The second structure proposes a modification to the operational setpoint of the system's control loop. These modulations are obtained from an optimization algorithm making it possible to achieve a trade-off between the dynamic performance requirement and the RUL criteria. The optimization algorithm is based on the prediction of the RUL and on the estimation of the deterioration. A numerical example illustrates the use of each of these two strategies, through the results of estimating the deterioration, predicting RUL, and obtaining the modulation parameter from the optimization. The RUL extension and its impact on performance trade-off are illustrated to evaluate the performances of both strategies. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Failure Prognostic optimization and control Remaining Useful Life Degradation

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Simulation of Remote Manipulator control System with Saturated Actuator 10th

Simulation of Remote Manipulator Control System with Saturat...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and control (MIM)

作者： Zaitceva, Iuliia Andrievsky, Boris Kuznetsov, Nikolay, V Popov, Alexander M. St Petersburg State Univ St Petersburg 199034 Russia Russian Acad Sci Inst Problems Mech Engn St Petersburg 199178 Russia Baltic State Tech Univ Voenmeh St Petersburg 190005 Russia

The paper is devoted to a human operator control for an electromechanical remote manipulator. It is assumed that a human operator in a closed-loop achieves the goal of control by deflecting the joystick. A saturation nonlinearity is taken into account in the paper. To compensate for the negative phase delay in the system performance, a corrective filter is introduced to the actuator loop. For such a nonlinear system the identification algorithm of the parameters of the human operator model is proposed. The algorithm relies on an optimization method with constraints imposed on the control system parameters. The simulation results demonstrate the dependence of human operator model parameters on the controller gain. The nonlinear filter application in a control loop with a saturated actuator demonstrates an improvement of the system performance and the oscillations prevention of the output coordinate. Copyright (C) 2022 The Authors.

关键词： Modeling simulation control monitoring of manufacturing processes optimization and control robotics in manufacturing

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Deep Reinforcement Learning for Robotic control with Multi-Fidelity Models 2nd

Deep Reinforcement Learning for Robotic Control with Multi-F...

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2nd Modeling, Estimation and control Conference (MECC)

作者： Leguizamo, David Felipe Yang, Hsin-Jung Lee, Xian Yeow Sarkar, Soumik Iowa State Univ Ames IA 50010 USA

Deep reinforcement learning (DRL) can be used for the development of robotic controllers. Complicated kinematic relationships can be learned by a DRL agent, which will result in a control policy that takes actions based on an observed state. However, a DRL agent typically goes through much trial and error before beginning to take appropriate actions. Therefore, it is often useful to leverage simulated robotic manipulators before performing any training or testing on actual hardware. There are several options for such simulation, ranging from simple kinematic models to more complex models seeking to accurately simulate the effects of gravity, inertia, and friction. The latter models can provide excellent representations of a robotic plant, but typically with a noticeably increased computational expense. Reducing the expense of simulating the robotic plant (while still maintaining a reasonable degree of accuracy) can accelerate an already expensive DRL training loop. In this work, we present a methodology for using a lower-fidelity model (based on Denavit-Hartenberg parameters) to initialize the training of a DRL agent for control of a Sawyer robotic arm. We show that the trained DRL policy can then be fine-tuned in a higher-fidelity simulation provided by the robot's manufacturer. We demonstrate the accuracy of the fully trained policy by transferring it to the actual hardware, demonstrating the power of DRL to learn complicated robotic tasks entirely in simulation. Finally, we benchmark the time required to train a policy using each level of fidelity. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Robotic Systems Self-learning Models Real-time Artificial Intelligence Reinforcement learning control Engineering Applications of Artificial Intelligence optimization and control Multi-Fidelity Modeling

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Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem

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APPLIED SOFT COMPUTING 2021年 100卷

作者： Deng, Wu Xu, Junjie Song, Yingjie Zhao, Huimin Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China Shandong Technol & Business Univ Coinnovat Ctr Shandong Coll & Univ Future Intelli Yantai 264005 Peoples R China Southwest Jiaotong Univ Tract Power State Key Lab Chengdu 610031 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

The optimization performance of differential evolution(DE) algorithm significantly depends on control parameters and mutation strategy. However, it is difficult to set suitable control parameters and select reasonable mutation strategy for DE in solving an actual engineering optimization problem. To solve these problems, a new optimal mutation strategy based on the complementary advantages of five mutation strategies is designed to develop a novel improved DE algorithm with the wavelet basis function, named WMSDE, which can improve the search quality, accelerate convergence and avoid fall into local optimum and stagnation. In the proposed WMSDE, the initial population is divided into several subpopulations to exchange search information between the different subpopulations and improve the population diversity to a certain extent. The wavelet basis function and normal distribution function are used to control the scaling factor and the crossover rate respectively in order to ensure the diversity of solutions and accelerate convergence. The new optimal mutation strategy is used to improve the local search ability and ensure the global search ability. Finally, the proposed WMSDE is compared with five state-of-the-art DE variants by 11 benchmark functions. The experiment results indicate that the proposed WMSDE can avoid premature convergence, balance local search ability and global search ability, accelerate convergence, improve the population diversity and the search quality. Additionally, a real-world airport gate assignment problem is employed to further prove the effectiveness of the proposed WMSDE. The results show that it can effectively solve the complex airport gate assignment problem, and obtain airport gate assignment rate of 97.6%. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Differential evolution Wavelet basis function Optimal mutation strategy optimization and control Gate assignment

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An Integrated Biomimetic control Strategy with Multi-agent optimization for Nonlinear Chemical Processes⁎

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IFAC-PapersOnLine 2018年第18期51卷 55-60页

作者： Mirlekar, Gaurav Gebreslassie, Berhane H. Li, Shuyun Diwekar, Urmila M. Lima, Fernando V. Department of Chemical and Biomedical Engineering West Virginia University MorgantownWV26506 United States Vishwamitra Research Institute Clarendon Hills IL60514 United States

In this paper, a framework is proposed for integrating a Biologically-Inspired Optimal control Strategy (BIO-CS) with Multi-Agent optimization (MAO) algorithms for process systems engineering applications. In this framework, the BIO-CS employs gradient-based optimal control solvers in an intelligent manner to simultaneously control multiple outputs of the process at their desired setpoints. Also, the MAO uses the capabilities of nonlinear heuristic-based optimization techniques such as Efficient Ant Colony optimization (EACO), Efficient Genetic Algorithm (EGA) and Efficient Simulated Annealing (ESA) by sharing process information to obtain as an upper layer optimal operating setpoints for the controller that satisfy the overall process objective. The resulting approach is a unique combination of control and optimization methods that provide optimal solutions for dynamic systems. The applicability of the proposed framework is demonstrated using a nonlinear, multivariable fermentation process. In particular, a multivariable control structure associated with the first-principles-based model derived from mass and energy balances of the fermentation process is addressed. The performance of the proposed approach for each step is compared to Sequential Quadratic Programming (SQP) and a classical Proportional-Integral (PI) controller in terms of optimization and control, respectively. The proposed approach improves the overall performance of the process in terms of cumulative production rate by approximately 10-15%, resulting in economic benefits. The obtained results illustrate the capabilities of this novel integrated framework to achieve desired nonlinear system performance considering scenarios associated with setpoint tracking and plant-model mismatch. © 2018

关键词： Multi agent systems Agents Ant colony optimization Biomimetic processes controllers Fermentation Genetic algorithms Intelligent control Optimal control systems optimization Process control Quadratic programming Simulated annealing Two term control systems control and optimization Efficient genetic algorithms Non linear control Optimal controls optimization and control Process systems engineering Proportional integral controllers Sequential quadratic programming

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