Multiobjective optimisation method for life-cycle design of mechanical products

Manufacturing that minimises the exhaustion of natural resources, energy used and deleterious environmental impact is increasingly demanded by societies that seek to protect global environments as much as possible. To achieve this, life-cycle design (LCD) is an essential component of product design scenarios; however, LCD approaches have not been well integrated in optimal design methods that support quantitative decision-making. This study presents a method that yields quantitative solutions through optimisation analysis of a basic product design incorporating life-cycle considerations. We consider two types of optimisation approaches that have different aims, namely, (1) to reduce the use of raw materials and energy consumption and (2) to facilitate the reuse of the product or its parts when it reaches the end of its useful life. We also focus on how the optimisation results differ according to the approach used, from the viewpoint of the 3R concept (Reduce, Reuse and Recycling). Our method obtains optimum solutions by evaluating objectives fitted to each of these two optimisation approaches with respect to the product's life-cycle stages, which are manufacturing, use, maintenance, disposal, reuse and recycling. As an applied example, a simple linear robot model is presented, and Pareto optimum solutions are obtained for the multiobjective optimisation problem whose evaluated objectives are the operating accuracy of the robot and the different life-cycle costs for the two approaches. The characteristics of the evaluated objectives and design variables, as well as the effects of using material characteristics as design parameters, are also examined.     

Decision support for sustainable manufacturing using decision guidance query language

Sustainability has become a very significant research topic as it impacts many different manufacturing industries. Therefore, the technologies for monitoring, analysing, evaluating and optimising the sustainability performance of manufacturing processes and systems are very critical for decision makers on the shop floor. This paper introduces a decision guidance management system that provides actionable recommendations through quantitative analysis of the sustainability measures of manufacturing processes and systems based on life cycle assessment. The system determines decision preferences through dynamically collected data and decision makers' responses, taking into account the prevailing constraints. Optimal decisions can be derived using mathematical and constraint programming. By using decision guidance query language, this methodology allows users to make optimal decisions without an extensive mathematical or operations research background. Knowledge of relational databases is sufficient for a user to formulate the optimisation problem and obtain optimal solutions. The methodology is demonstrated with a machining operation case study, in which a list of sustainability metrics are identified and sustainability modelling methods are proposed. Important sustainable machining performance measures are optimised, resulting in actionable recommendations.     

SME application of LCA‐based carbon footprints

Following a brief introduction about the need for businesses to respond to climate change, this paper considers the development of the phrase ‘carbon footprint’. Widely used definitions are considered before the authors offer their own interpretation of how the term should be used. The paper focuses on the contribution small and medium sized enterprises (SMEs) make to the economy and their level of influence in stimulating change within organisations. The experience of an outreach team from the Engineering Department of a UK university is used which draws on the experience of delivering regional economic growth projects funded principally through the European Regional Development Fund. Case studies are used including the development of bespoke carbon footprints for SMEs from an initiative delivered by the outreach team. Limitations of current carbon footprints are identified based on this higher education‐industry knowledge exchange mechanism around three main themes of scope, the assessment method and conversion factors. Evidence and discussions are presented that conclude with the presentation of some solutions based on the work undertaken with SMEs and a discussion on the merits of the two principally used methodologies: life‐cycle analysis and economic input–output assessment.     

氢燃料电池汽车动力系统生命周期评价及关键参数对比

发展氢燃料电池汽车被认为是解决能源安全和环境污染问题的理想解决方案之一,为量化探究氢燃料电池汽车动力系统的化石能源消耗和排放情况,运用GaBi软件建模,以新能源汽车相关技术路线为参考,构建我国氢燃料电池汽车动力系统的数据清单并对其全生命周期化石能源消耗和全球变暖潜值情况进行定量评价计算和预测分析,对不同类型的双极板、不同能量控制策略和不同制氢方式对环境的影响分别进行了对比研究,并对关键数据进行了不确定分析.结果表明,预计到2030年我国每台氢燃料电池汽车动力系统生命周期的化石能源消耗量（ADP_f）、全球变暖潜值（GWP,以CO₂ eq计）和酸化潜值（AP,以SO₂ eq计）分别为1.35×10⁵ MJ、9108 kg和15.79 kg.动力系统生产制造阶段的化石能源消耗和全球变暖潜值均高于使用阶段,主要原因是燃料电池堆栈和储氢罐的制造过程.金属双极板、石墨复合双极板和石墨双极板的制造工艺中石墨复合双极板的综合环境效益最好.能量控制策略的优化会使得氢能消耗降低,当氢能消耗降低22.8%时,动力系统的生命周期化石能源消耗和全球变暖潜值分别降低10.4%和8.3%.相比于甲烷蒸气重整制氢,基于混合电网电解水制氢的动力系统生命周期全球变暖潜值高出53.7%[KG-*6],而基于水电电解水制氢降低39.6%.降低动力系统生命周期化石能源消耗和全球变暖潜值的措施包括优化能量控制策略降低氢能消耗、规模化发展可再生能源发电电解水制氢产业和聚焦突破燃料电池堆栈关键技术实现性能提升.     

Priority screening of toxic chemicals and industry sectors in the U.S. toxics release inventory: a comparison of the life cycle impact-based and risk-based assessment tools developed by U.S. EPA

Life Cycle Impact Assessment (LCIA) and Risk Assessment (RA) employ different approaches to evaluate toxic impact potential for their own general applications. LCIA is often used to evaluate toxicity potentials for corporate environmental management and RA is often used to evaluate a risk score for environmental policy in government. This study evaluates the cancer, non-cancer, and ecotoxicity potentials and risk scores of chemicals and industry sectors in the United States on the basis of the LCIA- and RA-based tools developed by U.S. EPA, and compares the priority screening of toxic chemicals and industry sectors identified with each method to examine whether the LCIA- and RA-based results lead to the same prioritization schemes. The Tool for the Reduction and Assessment of Chemical and other environmental Impacts (TRACI) is applied as an LCIA-based screening approach with a focus on air and water emissions, and the Risk-Screening Environmental Indicator (RSEI) is applied in equivalent fashion as an RA-based screening approach. The U.S. Toxic Release Inventory is used as the dataset for this analysis, because of its general applicability to a comprehensive list of chemical substances and industry sectors. Overall, the TRACI and RSEI results do not agree with each other in part due to the unavailability of characterization factors and toxic scores for select substances, but primarily because of their different evaluation approaches. Therefore, TRACI and RSEI should be used together both to support a more comprehensive and robust approach to screening of chemicals for environmental management and policy and to highlight substances that are found to be of concern from both perspectives.     

Evaluation of nitrogen retention and microbial populations in poultry litter treated with chemical, biological or adsorbent amendments

Poultry litter is a valuable nutrient source for crop production. Successful management to reduce ammonia and its harmful side-effects on poultry and the environment can be aided by the use of litter amendments. In this study, three acidifiers, two biological treatments, one chemical urease inhibitor and two adsorber amendments were added to poultry litter. Chemical, physical and microbiological properties of the litters were assessed at the beginning and the end of the experiment. Application of litter amendments consistently reduced organic N loss (0-15%) as compared to unamended litter (20%). Acidifiers reduced nitrogen loss through both chemical and microbiological processes. Adsorbent amendments (water treatment residuals and chitosan) reduced nitrogen loss and concentrations of ammonia-producing bacteria and fungi. The use of efficient, cost-effective litter amendments to maximum agronomic, environmental and financial benefits is essential for the future of sustainable poultry production.     

中央-地区二元结构的煤炭城市可持续发展战略选择与路径

煤炭在未来相当长的时间内仍是我国的基本能源.构建了中央-地区二元结构的煤炭城市可持续发展战略匹配集,根据煤炭资源的开采程度将煤炭城市分成幼年期、成长期、成熟期、衰退期四个阶段,基于三维综合判断对不同阶段的煤炭城市提出了不同的战略选择,明晰了不同发展战略的战略目标及发展的支持性政策措施.     

Modelling above- and below-ground mass loss and N dynamics in wooden dowels (LIDET) placed across North and Central America biomes at the decadal time scale

This article focuses on modelling above and below-ground mass loss and nitrogen (N) dynamics based on the wooden dowels (Gonystylus bancanus [Miq.] Kurz) of the decadal Long-term Intersite Decomposition Experiment (LIDET) data. These dowels were placed at 27 locations across North and Central America, involving tropical, temperate and boreal forests, grasslands, wetlands and the tundra. The dowel, inserted vertically into the soil with one half remaining exposed to the air, revealed fast mass and N losses under warm to humid conditions, and slow losses under wet as well as cold to dry conditions. The model formulation, referred to as the Wood Decomposition Model, or WDM, related these losses to (i) mean annual precipitation, mean monthly January and July air temperatures, and (ii) mean annual actual evapotranspiration (AET) at each location. The resulting calibrations conformed well to the time-in-field averages for mass remaining by location: R² = 0.83 and 0.90 for the lower and upper parts, respectively. These values dropped, respectively, to 0.41 and 0.55 for the N concentrations, and to 0.28 and 0.43 for N remaining. These reductions likely refer to error propagation and to as yet unresolved variations in N transference into and out of the wood specific to each individual dowel location. Recalibrating the model parameters by ecosystem type reduced the R² values for actual versus best-fitted mass loss by about 0.15. Doing the same without location- or ecosystem-specific adjustments reduced the R² values further, by about 0.3.     

Modelling forest management within a global vegetation model—Part 2: Model validation from a tree to a continental scale

The construction of a new forest management module (FMM) within the ORCHIDEE global vegetation model (GVM) allows a realistic simulation of biomass changes during the life cycle of a forest, which makes many biomass datasets suitable as validation data for the coupled ORCHIDEE-FM GVM. This study uses three datasets to validate ORCHIDEE-FM at different temporal and spatial scales: permanent monitoring plots, yield tables, and the French national inventory data. The last dataset has sufficient geospatial coverage to allow a novel type of validation: inventory plots can be used to produce continuous maps that can be compared to continuous simulations for regional trends in standing volumes and volume increments. ORCHIDEE-FM performs better than simple statistical models for stand-level variables, which include tree density, basal area, standing volume, average circumference and height, when management intensity and initial conditions are known: model efficiency is improved by an average of 0.11, and its average bias does not exceed 25%. The performance of the model is less satisfying for tree-level variables, including extreme circumferences, tree circumference distribution and competition indices, or when management and initial conditions are unknown. At the regional level, when climate forcing is accurate for precipitation, ORCHIDEE-FM is able to reproduce most productivity patterns in France, such as the local lows of needleleaves in the Parisian basin and of broadleaves in south-central France. The simulation of water stress effects on biomass in the Mediterranean region, however, remains problematic, as does the simulation of the wood increment for coniferous trees. These pitfalls pertain to the general ORCHIDEE model rather than to the FMM. Overall, with an average bias seldom exceeding 40%, the performance of ORCHIDEE-FM is deemed reliable to use it as a new modelling tool in the study of the effects of interactions between forest management and climate on biomass stocks of forests across a range of scales from plot to country.