The manufacturing industry plays a pivotal role in an industrial society due to its enormous contribution to the economy, environment and innovation. However manufacturing is also associated with large share of energy consumption. In US, industrial sector was responsible for 22% of total energy consumed in 2012.

In order to understand the environmental impacts of manufacturing sector this session focuses on LCA methodology as applied to the current and future manufacturing technologies demonstrated through 4 case studies which range from advanced manufacturing to smart manufacturing. The issues faced during scaling up novel technologies have also been discussed in this session along with potential solutions.

LOCATION: 2306
10:30

LCA considering a scale effect: case studies on two future technologies
SPEAKER: Kotaro Kawajiri

ABSTRACT. The objective of this study is to investigate the method to consider a scale effect in LCA through the case studies of future technologies. The environmental impacts of laboratory scale future technology using small scale processes are much larger than those of conventional technology using the large scale mass production processes because an efficiency of small scale process is generally lower than that of large scale process. Therefore, the extension of LCA to consider a scale effect is necessary to compare the environmental impacts of laboratory scale technology to those of commercial scale conventional technologies. In this study, we propose a method to consider a scale effect in LCA. The following two cradle-to-gate LCA case studies are conducted to demonstrate our method; carbon nanotube super growth method [1] and oxidation process using hydrogen peroxide [2]. The CO2 impacts of the two technologies are estimated at the following three different scales; laboratory scale, pilot plant scale, and commercial scale. The inventory data of laboratory scale is obtained from the researchers. That of pilot plant is estimated from the patent data [3] and report [4]. That of commercial scale is estimated from the inventory of pilot plant using scaling factor [5]. The scaling factor is calculated using the machinery specification data on the website [6]. In this study, we consider the scale effect of energy consumption. CO2 impacts are estimated using the IDEA database [7]. The CO2 impacts to produce 1kg carbon nanotube using super growth method become 1/3 when the scale changes from laboratory to commercial. The CO2 impacts to oxide 1kg alpha-pinene using hydrogen peroxide become 1/700 when the scale changes from laboratory to commercial. The results imply that the scale effect is critical to compare the environmental impacts of laboratory scale future technology to those of the commercial scale conventional technology. Considering the scale effect, it becomes possible to estimate the potential of future technology in LCA framework.

[1] K. Hata, D. N. Futaba, K. Mizuno, T. Namai, M. Yumura, S. Iijima, Science, 2004, pp. 1362-1364. [2] Y. Kon, K. Sato, AIST TODAY, 2012, pp. 12. [3] Japan patent JP 4581146 [4] NEDO, Development of Fundamental Technologies for Green and Sustainable Chemical Processes/Development of Innovative Chemical Process-Product with Less Waste Emission Fundamental Technology/Development of Innovative Oxidation Process (FY2009-FY2011), Final Report Summary. [5] G. F. Nemet, Energy Policy, 34 (2006), pp. 3218-3232. [6] Alibaba, http://www.alibaba.com/ [7] K. Tahara et al, Proc. 9th Meeting of the Institute of Life Cycle Assessment, Japan, 2013, pp. 174-175.

10:45

Prospective environmental and economic assessment of advanced manufacturing technologies: a multi-scale life cycle approach
SPEAKER: Runze Huang

ABSTRACT. The development of advanced manufacturing technologies is essential in enhancing U.S. competitiveness as well as reducing industrial life-cycle energy consumption over next decades [1][2][3]. Such development is majorly driven by investment from governmental authorities and industries; therefore understanding the potential environmental and economic implications of the advanced manufacturing technologies is useful in better related decision-makings.

In this study, a life cycle modeling framework is developed to assess the net impact of emerging manufacturing technologies. Aspects that matter to current manufacturing industry are evaluated, including life cycle energy consumption, GHG emissions, costs, revenue, and manufacturing leading time. A multi-scale approach is explored by comparing results at process, facility and industry level as well as different spatial and temporal scales. Integrated Life cycle assessment (LCA) with techno-economic analysis is used to quantify the net changes of manufacturing systems and corresponding national impacts. Model results provide stakeholders at different levels with a good understanding of potential energy savings, GHG emission reductions, and economic benefits through adopting advanced manufacturing technologies, as well as identifying technology development opportunities that maximize these benefits.

For model application, additive manufacturing technologies are applied to different industries and compared with conventional manufacturing processes. In the case study of U.S. passenger aircraft industry, the results show a cumulative fleet-wide life-cycle primary energy savings of 1.2-2.8 billion GJ and 92-215 million metric tons of GHG emissions by 2050. In the case study of tooling, the model displayed that potentially 36% of the primary energy, 51% CO2-e emissions, 54% lead-time and 34% life cycle cost could be saved, and identified that improving machine throughput as well as reducing raw material (metal powder) cost could speed up achieving these benefits.

The modeling framework is a useful tool for decision makers in technology investment. The multi-scale approach and integrated analysis provides necessary detailed insights at different levels for stakeholders with distinguished preferences, such as researchers, managers, and policy makers. These outcomes are helpful to improve U.S. manufacturing in reducing energy and resource consumptions through emerging manufacturing technologies.

[1] Council on Competitiveness, Make, An American Manufacturing Movement, 2011 [2] National Science and Technology Council, A National Strategic Plan for Advanced Manufacturing, 2012 [3] President’s Council of Advisors on Science and Technology, Report to the President on Ensuring Leadership in Advanced Manufacturing, 2011

11:00

Environmental Impacts of Additive Manufacturing

ABSTRACT. The GE Ecoassessment Center of Excellence has been applying LCA to explore the environmental impacts, benefits, and trade-offs of additively manufactured aircraft engine parts compared to traditional machined or cast parts. This presentation will explore a case study focused primarily on GE Aviation’s additively manufacturing fuel nozzle for the CFM LEAP engine. The fuel nozzle is traditionally manufactured by forging and machining in which the desired part shape is achieved via removal of material from a solid metal ingot and then multiple parts are joined together. Additive manufacturing involves building the desired part shape by adding material layer by later, in this case by direct laser melting to fuse metal powders. The additive manufacturing approach offers the potential for novel part geometries that result in reduced life cycle environmental impact due to enhanced performance (e.g., reduced fuel consumption over the life of the engine due to lower weight) and net lower raw material consumption.

GE has been piloting the US Department of Defense draft Sustainability Assessment methodology, which incorporates streamlined life cycle assessment/life cycle costing (SLCA/LCC) into the acquisitions process. The LCA portion of the assessment is a hybrid approach involving environmental input-output data for the upstream (supply chain) impacts and activity-based scoring factors for the downstream (operation, sustainment, end of life) impacts.

Since the use of additive manufacturing at a commercial scale represents a significant change affecting manufacturing techniques, materials choices and supply chain, GE Aviation is also interested in understanding supply chain and manufacturing impacts in greater detail using traditional process LCA. We have therefore been performing process LCA in parallel with the US DoD SLCA/LCC method.

This presentation will provide an overview of the SLCA/LCC and process-LCA approaches used, and discuss how the insights gained can be leveraged for both supplier (GE Aviation) and customer (US DoD).

11:15

The Rise of Smart Manufacturing: Opportunities and Challenges for LCA

ABSTRACT. The rapid growth of information and communication technology (ICT) has the potential to transform the industrial sector through Smart Manufacturing, where networked sensors, controls, and platforms are applied for the optimization of manufacturing processes. The use of ICT to create real-time data collection, diagnostics, analytics, and optimization can enable dynamic performance management, improved process accuracy, and energy and cost savings. We show how the adoption of Smart Manufacturing technology in the industrial sector will create new demands and opportunities for Life-Cycle Assessment. First, we discuss how the development of Smart Manufacturing technologies, and the application of these technologies in the industrial sector, will have large impacts when applying LCA to manufacturing equipment, manufactured products, and product supply chains. Along with changes within specific industrial sectors, our framework expands the scope of industrial LCA to include ICT infrastructure, from on-site IT equipment to data centers and house processing, storage, and network equipment. Second, we highlight the need for new LCA models for use in optimization at the factory and supply chain level. Lastly, the deployment of Smart Manufacturing systems will generate scores of data on energy usage, equipment utilization and maintenance, and material consumption and waste that can create opportunities to conduct new LCA with exceptional accuracy. We outline a national strategy to collect and apply this data, propose possible public and private sector roles and activities, and discuss the key barriers to implementation.