An integrated method for sustainable manufacturing systems design

In the past decade, there has been an increasing awareness in development of sustainable manufacturing systems as governments in many countries have been enforcing ever-stricter environmental policies and regulations in industry by promoting energy saving and low emissions manufacturing activities. Lean manufacturing can be helpful for achieving a sustainable manufacturing system as it can reduce production wastes and increase manufacturing efficiency. Nevertheless, this lean approach does not include a consideration in energy consumption and carbon dioxide (CO2) emissions when designing a lean manufacturing system. This paper presents a methodology which can be useful for measuring energy consumption and CO2 emissions for a typical manufacturing system design at an early stage. A case study was carried out for obtaining computational results using the developed methodology based on data collected from a real production line

Sustainability Benefits Analysis of CyberManufacturing Systems

Confronted with growing sustainability awareness, mounting environmental pressure, meeting modern customers’ demand and the need to develop stronger market competitiveness, the manufacturing industry is striving to address sustainability-related issues in manufacturing. A new manufacturing system called CyberManufacturing System (CMS) has a great potential in addressing sustainability issues by handling manufacturing tasks differently and better than traditional manufacturing systems. CMS is an advanced manufacturing system where physical components are fully integrated and seamlessly networked with computational processes. The recent developments in Internet of Things, Cloud Computing, Fog Computing, Service-Oriented Technologies, etc., all contribute to the development of CMS. Under the context of this new manufacturing paradigm, every manufacturing resource or capability is digitized, registered and shared with all the networked users and stakeholders directly or through the Internet. CMS infrastructure enables intelligent behaviors of manufacturing components and systems such as self-monitoring, self-awareness, self-prediction, self-optimization, self-configuration, self-scalability, self-remediating and self-reusing. Sustainability benefits of CMS are generally mentioned in the existing researches. However, the existing sustainability studies of CMS focus a narrow scope of CMS (e.g., standalone machines and specific industrial domains) or partial aspects of sustainability analysis (e.g., solely from energy consumption or material consumption perspectives), and thus no research has comprehensively addressed the sustainability analysis of CMS. The proposed research intends to address these gaps by developing a comprehensive definition, architecture, functionality study of CMS for sustainability benefits analysis. A sustainability assessment framework based on Distance-to-Target methodology is developed to comprehensively and objectively evaluate manufacturing systems’ sustainability performance. Three practical cases are captured as examples for instantiating all CMS functions and analyzing the advancements of CMS in addressing concrete sustainability issues. As a result, CMS has proven to deliver substantial sustainability benefits in terms of (i) the increment of productivity, production quality, profitability & facility utilization and (ii) the reduction in Working-In-Process (WIP) inventory level & material consumption compared with the alternative traditional manufacturing system paradigms

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved

Simulation Modeling for Energy-Flexible Manufacturing: Pitfalls and How to Avoid Them

Due to the high share of industry in total electricity consumption, industrial demand-side management can make a relevant contribution to the stability of power systems. At the same time, companies get the opportunity to reduce their electricity procurement costs by taking advantage of increasingly fluctuating prices on short-term electricity markets, the provision of system services on balancing power markets, or by increasing the share of their own consumption from on-site generated renewable energy. Demand-side management requires the ability to react flexibly to the power supply situation without negatively affecting production targets. It also means that the management and operation of production must consider not only production-related parameters but also parameters of energy availability, which further increase the complexity of decision-making. Although simulation studies are a recognized tool for supporting decision-making processes in production and logistics, the simultaneous simulation of material and energy flows has so far been limited mainly to issues of energy efficiency as opposed to energy flexibility, where application-oriented experience is still limited. We assume that the consideration of energy flexibility in the simulation of manufacturing systems will amplify already known pitfalls in conducting simulation studies. Based on five representative industrial use cases, this article provides practitioners with application-oriented experiences of the coupling of energy and material flows in simulation modeling of energy-flexible manufacturing, identifies challenges in the simulation of energy-flexible production systems, and proposes approaches to face these challenges. Seven pitfalls that pose a particular challenge in simulating energy-flexible manufacturing have been identified, and possible solutions and measures for avoiding them are shown. It has been found that, among other things, consistent management of all parties involved, early clarification of energy-related, logistical, and resulting technical requirements for models and software, as well as the application of suitable methods for validation and verification are central to avoiding these pitfalls. The identification and characterization of challenges and the derivation of recommendations for coping with them can raise awareness of typical pitfalls. This paper thus helps to ensure that simulation studies of energy-flexible production systems can be carried out more efficiently in the future

Sustainable innovation: key conclusions from Sustainable Innovation Conferences 2003–2006 organised by The Centre for Sustainable Design

The following is taken directly from the introduction. This booklet summarises the key conclusions from the 2003–2006 conferences on Sustainable Innovation organised by The Centre for Sustainable Design (www.cfsd.org.uk). The conclusions are drawn from the respective conference presentations, papers and discussions. The publication has been sponsored as part of a ‘Centre of Excellence in Sustainable Innovation & Design’ project awarded to The Centre for Sustainable Design by the South-East England Development Agency (SEEDA)

The role of public policy in stimulating radical environmental impact reduction in the automotive sector: The need to focus on product-service system innovation

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2010 InderscienceProduct-service system (PSS) innovation is a promising approach to address sustainability challenges in the automotive industry. Starting form this assumption, this paper presents and discusses the potential contribution that policy measures can have in fostering the automotive sector in innovating on a PSS level. A set of policy instruments (general instruments and specific PSS-targeted ones) are presented and classified, underlining the effects they could produce at the company and environmental levels. In order to effectively support sustainable PSS diffusion in the automotive industry, the paper suggests the integration of general policy measures (such as internalisation of external costs, extended producer responsibility programmes and informative policies), with the PSS-targeted ones (such as Green Public Procurement focused on sustainable PSS, support of companies in acquiring information related to PSS, support of demonstrative pilot projects). In addition, the paper suggests the necessity to involve actively universities and research centres

Life cycle energy and carbon analysis of domestic combined heat and power generators

Micro Combined Heat and Power (micro-CHP) generators combine the benefits of the high-efficiency cogeneration technology and microgeneration and is being promoted as a means of lowering greenhouse gas emissions by decentralizing the power network. Life Cycle Assessment of energy systems is becoming a part of decision making in the energy industry, helping manufacturers promote their low carbon devices, and consumers choose the most environmentally friendly options. This report summarizes a preliminary life-cycle energy and carbon analysis of a wall-hung gas-powered domestic micro-CHP device that is commercially available across Europe. Combining a very efficient condensing boiler with a Stirling engine, the device can deliver enough heat to cover the needs of a typical household (up to 24kW) while generating power (up to 1kW) that can be used locally or sold to the grid. Assuming an annual heat production of 20 MWh, the study has calculated the total embodied energy and carbon emissions over a 15 years operational lifetime at 1606 GJ and 90 tonnes of CO2 respectively. Assuming that such a micro CHP device replaces the most efficient gas-powered condensing boiler for domestic heat production, and the power generated substitutes electricity from the grid, the potential energy and carbon savings are around 5000 MJ/year and 530 kg CO2/year respectively. This implies a payback period of the embodied energy and carbon at 1.32 - 2.32 and 0.75 - 1.35 years respectively. Apart from the embodied energy and carbon and the respective savings, additional key outcomes of the study are the evaluation of the energy intensive phases of the device’s life cycle and the exploration of potential improvements