A framework for material flow assessment in manufacturing systems

Improving material efficiency is widely accepted as one of the key challenges facing manufacturers in the future. Increasing material consumption is having detrimental impacts on the environment as a result of their extraction, processing, and disposal. It is clear that radical improvements in material efficiency are required to avoid further environmental damage and sustain the manufacturing sector. Current resource management approaches are predominantly used to improve material consumption solely in economic terms. Meanwhile, environmental assessment methodologies can determine sources of significant environmental impact related to a product; however, a methodology to effectively assess material efficiency in production systems is currently not available. This paper highlights the benefits of material flow modeling within manufacturing systems to support advances in increased material efficiency, proposing a framework for “material flow assessment in manufacturing” that promotes greater understanding of material flow and flexibility to explore innovative options for improvement

Iron solubilization during anaerobic growth of acidophilic microorganisms with a polymetallic sulfide ore

Acidithiobacillus ferrooxidans at 30 °C and Sulfobacillus thermosulfidooxidans at 47 °C were selected from a preliminary screening of various acidophiles for their ferric iron reduction capacities during anaerobic, autotrophic growth on sulfur. The selected cultures were used with a polymetallic sulfide ore under anoxic conditions to demonstrate enhanced solubilization of iron during leaching in shaken flasks and enhanced removal of iron from laboratory ore-leaching columns, compared to leaching with continuous aeration. Ore-associated, ferric iron-rich precipitates, which were formed under previously oxidizing conditions, were a potential influence on extraction of target metals and percolation through ore columns and were available as the source of ferric iron for anaerobic sulfur oxidation. Over twice as much iron was removed by moderate thermophiles when anoxic phases were introduced during the leaching. Enhanced removal of iron and some improvement in extraction of base metals from ore fragments were also demonstrated with a selected "Sulfolobus"-like strain during growth and leaching with alternating periods of aeration and anoxic conditions at 70 °C

Energy-efficient systems for the sensing and separation of mixed polymers

Polymers are ubiquitous in modern manufactured products. The potential detrimental impacts of their end-of-life disposal have stimulated significant increases in recycling rates. Recyclate purity is paramount; however this must be achieved with a positive net energy balance. Existing technologies for identification and separation of polymers are often both expensive and energy intensive. This paper investigates Infrared (IR) imaging to extract information on thermal properties of various product polymers within a recycling line. An intelligent decision making support system is enabled using neural network based pattern recognition for automatic polymer identification and classification. Potential energy savings versus current technologies are discussed

A material flow modelling tool for resource efficient production planning in multi-product manufacturing systems

Resource efficiency is recognized as one of the greatest sustainability challenges facing the manufacturing industry in the future. Materials are a resource of primary importance, making a significant contribution to the economic costs and environmental impacts of production. During the manufacturing phase the majority of resource efficiency initiatives and management methodologies have been concerned primarily with improvements measured on an economic basis. More recently, the need for even greater levels of resource efficiency has extended the scope of these initiatives to consider complete manufacturing and industrial systems at an economic and environmental level. The flow of materials at each system level relates directly to material efficiency, which in turn influences the consumption of other resources such as water and energy. Initial research by the authors in material efficiency focused on material flow, proposing a material flow assessment approach, comprising a systematic framework for the analysis of quantitative and qualitative flow in manufacturing systems. The framework was designed to provide greater understanding of material flow through identification of strengths, weaknesses, constraints and opportunities for improvement, facilitating the implementation of improvement measures for greater efficiency in both environmental and economic terms. This paper presents an extension of this work, applying the material flow assessment framework to a complex multi-product and multi-site manufacturing system scenario. It begins with a description of the Resource Efficient Scheduling (RES) tool that supports the implementation of this framework. The tool models the interactions of quantitative and qualitative material flow factors associated with production planning and the resulting impacts on resource efficiency. This provides a more detailed understanding of the economic and resource impacts of different production plans, enabling greater flexibility and the ability to make better informed decisions. Finally a case study is presented, highlighting the application of the tool and its potential benefits

Optimized assembly design for resource efficient production in a multiproduct manufacturing system

Resource efficiency is one of the greatest challenges for sustainable manufacturing. Material flow in manufacturing systems directly influences resource efficiency, financial cost and environmental impact. A framework for material flow assessment in manufacturing systems (MFAM) was applied to a complex multi-product manufacturing case study. This supported the identification of options to alter material flow through changes to the product assembly design, to improve overall resource efficiency through eliminating resource intensive changeovers. Alternative assembly designs were examined using a combination of intelligent computation techniques: k-means clustering, genetic algorithm and ant colony algorithm. This provided recommendations balancing improvement potential with extent of process modification impact