Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Theoretical and Computational Research in Various Scheduling Models

Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field

Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies

This Special Issue was intended to contribute to the sustainable mobility agenda through enhanced scientific and multi-disciplinary knowledge to investigate concerns and real possibilities in the achievement of a greener mobility and to support the debate between industry and academic researchers, providing an interesting overview on new needs and investigation topics required for future developments

Topics in Demand Response for Energy Management in Smart Grid

Future electricity grids will enable greater and more sophisticated demand side participation, which refers to the inclusion of mechanisms that enable dynamic modification of electricity demand into the operations of the electricity market, known as Demand Response (DR). The underlying information-flow infrastructures provided by the emerging smart grid enhance the interactions between customers and the market, by which DR will improve electricity grids in several aspects, e.g., by reducing peak demand and reducing need for expensive peaker plants, or by enabling demand to follow supply such as those from volatile renewable resources, etc. Many types of appliances provide flexibilities in power usage which can be viewed as demand response resources, and how to exploit such flexibilities to achieve the benefits offered by DR is a central challenge. In this dissertation, we design algorithms and architectures to bridge the gap between scheduling appliances and the benefits that DR can bring to electricity grid by utilizing the smart grid\u27s underlying information infrastructure. First, we focus on demand response within the consumer premise, where an energy management controller (EMC) schedules appliance operation on behalf of customers to save energy cost. We propose an optimization-based control scheme for the EMC in the building that integrates both the operational flexible appliances such as clothes washer/dryer, dish washer and plug-in electric vehicles (PEVs), but also the thermostatically controlled appliances such as HVAC (heating, ventilation, and air conditioning) systems together with the thermal mass of the building. Model predictive control is employed to account for uncertainty in electricity prices and weather information. Under time-varying pricing, scheduling appliances smartly using our scheme can incur notable energy cost saving for customers. As an alternative, we also propose a communication-based control approach which is a joint appliance access and scheduling scheme in which the control algorithms are embedded into the communication protocols used by appliances. The control scheme is based on a threshold maximum power consumption set by the EMC; and we discuss how this threshold can be chosen so that it integrates the availability of local distributed renewable energy resources.Then we investigate demand response in the retail market level which involves interactions between customers and utilities. Pricing-based control and direct load control (DLC) are two types of approaches that are used or envisioned for this level. To address pricing based control methods, we propose real-time pricing (RTP) signals that can be designed to work with customer premise EMCs. The interaction between these EMCs and the pricing-setting utilities is modeled as a Stackelberg game. We demonstrate that our proposed RTP scheme reduces peak load and alleviates rebound peaks that are the typical shortcomings in existing pricing approaches. To address DLC methods, we propose a distributed DLC scheme based on a two-layer communication network infrastructure for large-scale, aggregate DR implementations. In the proposed scheme, average consensus algorithms are employed to distributively allocate control tasks amongst EMCs so that local appliance scheduling within each home will eventually achieve the aggregated control task, i.e., to alleviate mismatch between electricity supply and demand.Finally, we study how demand response affects the wholesale electricity market. As is conventional when studying interactions between electricity generators, we employ the Cournot game model to analyze how DR aggregators may impact wholesale energy markets. To do so, we assume that DR aggregators employ a computationally efficient, centralized scheduling mechanism to manage deferrable load over a large aggregate set of consumers. The load reduction from deferrable load can be seen as `generation\u27 in terms of balancing the market and is compensated as such under current regulatory mandates. Thus, the DR aggregator competes with other generators in a Cournot-Nash manner to make a profit in the wholesale market; and electricity prices are consequently reduced. We provide equilibrium analysis of the wholesale market that includes DR aggregators and demonstrate that under certain conditions the equilibrium exists and is unique

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Real-Time Analysis and Control of Serial Production Lines for Energy Efficient Manufacturing

Productivity analysis, operation control and energy consumption reduction have been the central topics in manufacturing research and practice. They are closely related to each other. Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems, while system performance analysis serves as the base of production control. On the other hand, effective operation control can result to energy efficiency in manufacturing. Steady state analysis has been investigated extensively; however, transient analysis remained largely unexplored. Our research focuses on system modeling, performance analysis, and real-time operation control of serial production lines with unreliable machines and finite buffers, especially in transient period, with Bernoulli or geometric reliability. Analytical results, practical case studies and applications for energy efficient manufacturing are provided. A simulation using ARENA software to reproduce and analyze brewery production line is performed

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd