Integrating environmental and economic life cycle analysis in product development: a material selection case study

Purpose Achieving sustainability by rethinking products, services and strategies is an enormous challenge currently laid upon the economic sector, in which materials selection plays a critical role. In this context, the present work describes an environmental and economic life cycle analysis of a structural product, comparing two possible material alternatives. The product chosen is a storage tank, presently manufactured in stainless steel (SST) or in a glass fibre reinforced polymer composite (CST). The overall goal of the study is to identify environmental and economic strong and weak points related to the life cycle of the two material alternatives. The consequential win-win or trade-off situations will be identified via a Life Cycle Assessment/Life Cycle Costing (LCA/LCC) integrated model. Methods The LCA/LCC integrated model used consists in applying the LCA methodology to the product system, incorporating, in parallel, its results into the LCC study, namely those of the Life Cycle Inventory (LCI) and the Life Cycle Impact Assessment (LCIA). Results In both the SST and CST systems the most significant life cycle phase is the raw materials production, in which the most significant environmental burdens correspond to the Fossil fuels and Respiratory inorganics categories. The LCA/LCC integrated analysis shows that the CST has globally a preferable environmental and economic profile, as its impacts are lower than those of the SST in all life cycle stages. Both the internal and external costs are lower, the former resulting mainly from the composite material being significantly less expensive than stainless steel. This therefore represents a full win-win situation. As a consequence, the study clearly indicates that using a thermoset composite material to manufacture storage tanks is environmentally and economically desirable. However, it was also evident that the environmental performance of the CST could be improved by altering its End-of-Life stage. Conclusions The results of the present work provide enlightening insights into the synergies between the environmental and the economic performance of a structural product made with alternative materials. Further, they provide conclusive evidence to support the integration of environmental and economic life cycle analysis in the product development processes of a manufacturing company, or in some cases even in its procurement practices.

Improving the environmental performance of bedding products by using life cycle assessment at the design stage

Improvement of the environmental performance of processes and products is a common objective in industry, and has been receiving increased attention in recent years. The main objective of this work is to evaluate the potential environmental impact of two bedding products, a polyurethane foam mattress (PFM) and a pocket spring mattress (PSM). These two types are the most common mattresses used in Europe. A Life Cycle Assessment (LCA) shows that the PFM has a higher environmental impact than the PSM. For both products the main cause of environmental impact is the manufacturing process, respectively the polyurethane foam block moulding process for the PFM, and the pocket spring nucleus process for the PSM. A scenario analysis shows the possibility of reducing the environmental impact of the products’ life cycle using an alternative End-of-Life scenario, resorting to incineration rather than landfill. Two strategies were also studied in order to reduce the environmental impact of the PFM: (1) reutilization of foam that was sent to the waste system management, and (2) a 20% weight reduction of the polyurethane foam. The second strategy has proven to be the most effective.

Fatigue Prediction on Fibrin Poly-ε-caprolactone Macroporous Scaffolds

Tissue engineering applications rely on scaffolds that during its service life, either for in-vivo or in vitro applications, are under mechanical solicitations. The variation of the mechanical condition of the scaffold is strongly relevant for cell culture and has been scarcely addressed. Fatigue life cycle of poly-ε-caprolactone, PCL, scaffolds with and without fibrin as filler of the pore structure were characterized both dry and immersed in liquid water. It is observed that the there is a strong increase from 100 to 500 in the number of loading cycles before collapse in the samples tested in immersed conditions due to the more uniform stress distributions within the samples, the fibrin loading playing a minor role in the mechanical performance of the scaffolds

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Conceptual design of a conveyer system for supporting basic quality of life of bedridden elderly people

With the increasing number of aged people, especially in developed countries, Ambient Assisted Living solutions have become an important subject to be explored and developed. Currently, as specialized Institutions in geriatric care cannot cope with the increasing requests for support of quality of life, patients have to remain at their homes having as caregiver the other member of the couple or a member of close family. A solution for supporting the caregiver, during assisting the bedridden person with some basic tasks as eating, taking a bath and/or hygiene care is of utmost importance. This paper presents an approach for supporting the caregiver in moving and repositioning the bedridden elderly people (BEP) with the assistance of a mechanical system conveyer. The conceptual design of the mechanical system must be devoted to assist the caregiver in the handling and repositioning of the BEP. The proposed mechatronic system must, ideally, minimize the system's handling complexity, reduce the number of caregivers and the amount of spended and needed effort.

Conceptual design of a mechatronic system for supporting basic quality of life of bedridden elderly people

Ambient Assisted Living is an important subject to be explored and developed, especially in developed countries, due to the increasing number of aged people. In this context the development of mechatronic support systems for bedridden elderly people (BEP) living in their homes is essential in order to support independence, autonomy and improve their quality of life. Some basic tasks as eating, taking a bath and/or hygiene cares become difficult to execute, regarding that often the main caregiver is the other element of the aged couple (husband or wife). This paper presents the conceptual design of a mechanical system especially devoted to assist the caregiver in the handling and repositioning of the BEP. Issues as reducing the number of caregivers, to only one, and reducing the system's handling complexity (because most of the time it will be used by an aged person) are considered. The expertise obtained from the visits to rehabilitation centers and hospitals, and from working meetings, are considered in the development of the proposed mechatronic system.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.