Zeyi Sun

Dynamic Energy Control for Energy Efficiency Improvement of Sustainable Manufacturing Systems Using Markov Decision Process

Greenhouse gas emissions and global warming have become vital problems to human society. About 40% of the carbon dioxide is emitted from electric power generation in the United States. Due to the lack of consideration in the system design and the lack of a real time systematic management method for energy consumption, the energy efficiency of industrial manufacturing systems is extremely low. Most of the existing research work related to energy efficiency improvement only focuses on a single-machine manufacturing system while little work has been done to achieve the optimal energy efficiency for a typical system with multiple machines and buffers. In this paper, an analytical model is developed to establish a systems (or holistic) view of energy efficiency in typical manufacturing systems with multiple machines and buffers that dynamically control energy consumption considering both energy states and production constraints. The complex interaction between the adopted energy control decisions and system state evolutions are modeled by Markov Decision Process. An approximate algorithm for the real time application is introduced to find a near-optimal solution. A numerical case study on a section of an assembly line is used to illustrate the effectiveness of the proposed approach.

Opportunity Estimation for Real-Time Energy Control of Sustainable Manufacturing Systems

Due to the complexity of modern manufacturing systems and the lack of optimal management of energy consumption, the energy efficiency of manufacturing systems in real industrial environment is much lower than designed level, which significantly increases operation cost, impedes company competitiveness in the global market, leads to high carbon dioxide emission, and results in destroyed environment and ecology. Compared with existing research efforts on energy management of single machine system in the literature, few works have been performed to study the opportunity for energy management of typical manufacturing systems with multiple machines and buffers. From the point-of-view of sustainability, considering stochastic factor and buffer utilization, this paper investigates the opportunity estimation for real-time energy control of typical multi-machine manufacturing systems without sacrificing system throughput. A numerical case study based on an automotive assembly line is used to illustrate the effectiveness and efficiency of the proposed method.

Real time electricity demand response for sustainable manufacturing systems: Challenges and a case study

Due to the mounting electricity demand from industrial sector of United States and the expected huge investment for new generation capacity, the significance of load management at the customer side has been gradually recognized by both academia and industry. Compared with many research efforts in long term production planning to shave the peak demand, seldom work on real time electricity demand response while maintaining system throughput for the purpose of grid stability for manufacturing company can be found. In this paper, a brief literature review on system load management is provided and the existing challenges of real time electricity demand response for manufacturing systems are analyzed. Numerical case study about demand response implementation is performed to illustrate the possibility of the significant energy consumption reduction without negative impact on system throughput through appropriate real-time production control.

Energy Consumption Modeling of Stereolithography-Based Additive Manufacturing Toward Environmental Sustainability

Summary Additive manufacturing (AM), also referred as three-dimensional printing or rapid prototyping, has been implemented in various areas as one of the most promising new manufacturing technologies in the past three decades. In addition to the growing public interest in developing AM into a potential mainstream manufacturing approach, increasing concerns on environmental sustainability, especially on energy consumption, have been presented. To date, research efforts have been dedicated to quantitatively measuring and analyzing the energy consumption of AM processes. Such efforts only covered partial types of AM processes and explored inadequate factors that might influence the energy consumption. In addition, energy consumption modeling for AM processes has not been comprehensively studied. To fill the research gap, this article presents a mathematical model for the energy consumption of stereolithography (SLA)-based processes. To validate the mathematical model, experiments are conducted to measure the real energy consumption from an SLA-based AM machine. The design of experiments method is adopted to examine the impacts of different parameters and their potential interactions on the overall energy consumption. For the purpose of minimization of the total energy consumption, a response optimization method is used to identify the optimal combination of parameters. The surface quality of the product built using a set of optimal parameters is obtained and compared with parts built with different parameter combinations. The comparison results show that the overall energy consumption from SLA-based AM processes can be significantly reduced through optimal parameter setting, without observable product quality decay.

Simulation-Based Energy Efficiency Improvement for Sustainable Manufacturing Systems

Energy efficiency improvement as well as carbon footprint reduction in the manufacturing industry becomes increasingly important for a green world from the point of sustainability. However, because of the complexity of modern manufacturing systems, most of the existing research efforts in energy efficiency improvement only focus on either single-machine system or process level. Seldom work has been performed to study the potential of energy consumption reduction for typical manufacturing systems with multiple machines and buffers. In this paper, a simulation-based method is proposed to study various strategies for energy efficiency improvement of complex manufacturing systems. This study provides an initial framework to study the real time energy control of multi-machine manufacturing systems, and demonstrates the energy efficiency improvement and energy saving potentials by adjusting the machines’ power level according to their operation states while maintaining the system throughput. Comparison between the results with and without power level adjustment is performed to illustrate the effectiveness of the proposed method.Copyright

Data driven production runtime energy control of manufacturing systems

Energy consumption and cost of manufacturing systems have been gradually considered as key performance indicators (KPIs) to evaluate the overall performance of manufacturing due to the increasing concerns of environmental protection and climate change. Different energy management methods have been developed for both specific manufacturing processes and entire manufacturing systems. Many of them are focused on optimal decision-making by offline calculations, while neglecting the online information for the real time decision-making. In this paper, we propose a data-driven energy control method that can provide manufacturers with a dynamic joint production and energy control scheme utilizing the online information regarding both production and energy to reduce energy consumption and cost while maintaining production. The proposed control schemes can be implemented and integrated into production runtime control system in manufacturing plants to save energy and its cost. A case study based on a real auto part manufacturing plant is conducted to illustrate the effectiveness of the proposed method.

Simulation-Based Electricity Demand Response for Combined Manufacturing and HVAC System Towards Sustainability

In this paper, we introduce a simulation-based method to implement electricity demand response for manufacturers considering both heating ventilation and air conditioning (HVAC) and manufacturing systems. Compared with the existing literature where the demand response implementation for manufacturing system and HVAC system is usually conducted separately, this paper advances the state of the art by combining two systems together in demand response. A joint simulation model is established using ProModel and EnergyPlus. ProModel is first used to simulate the demand response for the manufacturing system to obtain the demand response actions without influencing production. After that, these actions are used as input information in EnergyPlus where the HVAC and building models are developed. The interaction between the heat generated due to manufacturing machine operation and the indoor heat demand is explored. Three different demand response strategies, i.e., 1) the baseline model that neither manufacturing system nor HVAC is considered for demand response; 2) only manufacturing system is considered for demand response; and 3) both the HVAC and manufacturing systems are considered for demand response, are presented and compared. The results show that the combined model can achieve high power demand reduction during demand response event.© 2015 ASME

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

Conditional-based maintenance (CBM) decision-making is of high interests in recent years due to its better performance on cost efficiency compared to other traditional policies. One of the most respected methods based on condition-monitoring data for maintenance decision-making is Proportional Hazards Model (PHM). It utilizes condition-monitoring data as covariates and identifies their effects on the lifetime of a component. Conventional modeling process of PHM only treats the degradation process as a whole lifecycle. In this paper, the PHM is advanced to describe a multi-zone degradation system considering the fact that the lifecycle of a machine can be divided into several different degradation stages. The methods to estimate reliability and performance prognostics are developed based on the proposed multi-zone PHM to predict the remaining time that the machine stays at the current stage before transferring into the next stage and the remaining useful life (RUL). The results illustrate that the multi-zone PHM effectively monitors the equipment status change and leads to a more accurate RUL prediction compared with traditional PHM.Copyright

Simulation-based production scheduling with optimization of electricity consumption and cost in smart manufacturing systems

Due to the increasing awareness of environmental protection, the industrial sector is facing mounting pressure to reduce its energy consumption and carbon footprints. The application of energy management system for industrial plants is of high interests by industrial practitioners in their pursuit of high performance smart manufacturing systems. Many system integrators and solution providers have developed their own packages for energy management system for industrial plants. In this paper, we conduct a brief survey on the existing commercial packages of industrial energy management systems by different vendors. A future development direction regarding the functionality of decision-making of industrial energy management system is discussed and a method focusing on the decision-making of energy-integrated production scheduling is proposed. The performance metrics of energy consumption and cost as well as production throughput are modeled for operational performance optimization. A numerical case study of a real auto part manufacturing plant is presented to illustrate the benefits brought by the proposed method.