A comprehensive evaluation of physical and environmental performances for wet-white leather manufacture

This paper presents the comprehensive evaluation results of physical and environmental performances for a novel wet-white (chrome-free) leather manufacturing. The tanning process is optimized as 15 wt% tannic acid (TA) combination with 4 wt% Laponite nanoclay, giving the leather with shrinkage temperature (Ts) above 86 °C. Inductively coupled plasma-atomic emission spectrometry (ICP-AES) measurements indicate that Laponite can be evenly and tightly bound within the leather matrix, which is further confirmed by scanning electron microscopy and energy dispersive X-ray (SEM-EDX) spectroscopy analysis. The resultant wet-white leathers have reasonable good physical properties that can meet the standard requirements for furniture leather without containing hazardous Cr(VI) and formaldehyde. Further life cycle assessment (LCA) studies shows that tanning process is the main contributor to environmental impact categories in the wet-white tanning process, and tannic acid is the most significant substance factor. Compared to conventional chrome tanning, the wet-white tanning process exhibits much lower abiotic depletion potential (ADP), and reduced global warming potential (GWP) and human toxicity potential (HTP) impacts due to the nature of vegetable tanning; whereas, GWP excluding biogenic carbon and energy consumption are higher owing to prolonged run time.Peer ReviewedPostprint (published version

Measuring the Environmental Footprint of Leather Processing Technologies

Peer ReviewedPostprint (published version

Classification of textile samples using data fusion combining near- and mid-infrared spectral information

There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved.This research study was partially funded by Ministerio de Industria, Comercio y Turismo de España under grant number AEI-010400-2020-206 and by the Generalitat de Catalunya under grant numbers 2017 SGR 967 and 2017 SGR 828.Peer ReviewedPostprint (author's final draft

Single-use vs reusable transport packaging: a comparative life cycle analysis

This paper deals with a comparative analysis of two different packaging and transport scenarios, which exemplifies the mplications of choosing between single-use and reusable packaging. In particular, transport of a batch of chemicals by means of disposable fibre drums vs. reusable steel drums is investigated from a life cycle perspective, and the associated environmental impact in terms of Global Warming Potential, Acidification Potential, Gross Energy Requirement and solid waste generation is assessed. Results prove beyond reasonable doubt that, even in the case of durable packaging containers requiring the use of comparatively energy-intensive materials for their production, the reuse scenario is characterized by lower environmental impact indicators across the board, and as such is the most advisable and environmentally sound option.Postprint (author’s final draft

Disseny de reactors fotoquímics: descripció dels models d’emissió de llum per a reactors anulars. Part 1, models clàssics

El disseny d’un reactor químic ha de contemplar normalment,a no ser que puguin simplificar-se, una sèrie de balanços: el de matèria, el d’energia i el de quantitat de moviment. A més d’aquests, un model de reactor fotoquímic ha de considerar el balanç de radiació. Generalment, les equacions que defineixen aquests balanços estan acoblades i s’han de resoldre simultàniament. Aquest article explica com es planteja el balanç de radiació (amb les equacions matemàtiques i el suport gràfic necessari) per al reactor anular en medi homogeni quan la làmpada està centrada a l’eix del reactor. Concretament descriu els models d’emissió clàssics, que es caracteritzen per utilitzarcoordenades cartesianes amb els eixos centrats a la làmpada. El que es fa és presentar aquests models amb les equacions i el suport gràfic necessari per facilitar-ne la seva comprensió i homogeneïtzar la nomenclatura dels diferents models descrits

Portable instruments based on NIR sensors and multivariate statistical methods for a semiautomatic quality control of textiles

Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control.This research was partially funded by Generalitat de Catalunya under grant numbers ACE033/21/000028, 2021 SGR 00392, and 2021 SGR 01501.Peer ReviewedPostprint (published version

Optimal sizing of a hybrid grid-connected photovoltaic and wind power system

Hybrid renewable energy systems (HRES) have been widely identified as an efficient mechanism to generate electrical power based on renewable energy sources (RES). This kind of energy generation systems are based on the combination of one or more RES allowing to complement the weaknesses of one with strengths of another and, therefore, reducing installation costs with an optimized installation. To do so, optimization methodologies are a trendy mechanism because they allow attaining optimal solutions given a certain set of input parameters and variables. This work is focused on the optimal sizing of hybrid grid-connected photovoltaic-wind power systems from real hourly wind and solar irradiation data and electricity demand from a certain location. The proposed methodology is capable of finding the sizing that leads to a minimum life cycle cost of the system while matching the electricity supply with the local demand. In the present article, the methodology is tested by means of a case study in which the actual hourly electricity retail and market prices have been implemented to obtain realistic estimations of life cycle costs and benefits. A sensitivity analysis that allows detecting to which variables the system is more sensitive has also been performed. Results presented show that the model responds well to changes in the input parameters and variables while providing trustworthy sizing solutions. According to these results, a grid-connected HRES consisting of photovoltaic (PV) and wind power technologies would be economically profitable in the studied rural township in the Mediterranean climate region of central Catalonia (Spain), being the system paid off after 18 years of operation out of 25 years of system lifetime. Although the annual costs of the system are notably lower compared with the cost of electricity purchase, which is the current alternative, a significant upfront investment of over $10 M - roughly two thirds of total system lifetime cost - would be required to install such system. (C) 2015 Elsevier Ltd. All rights reserved.Postprint (author's final draft

Post-consumer textile waste classification through near-infrared spectroscopy, using an advanced deep learning approach

The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied— 50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90–100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale.This research was partially funded by the Ministerio de Industria, Comercio, y Turismo de España under grant number AEI-010400-2020-206, and by the Generalitat de Catalunya, under grant numbers 2017 SGR 967 and 2017 SGR 828.Peer ReviewedPostprint (published version

Inventory analysis and carbon footprint of coastland-hotel services: A Spanish case study

Tourism is a key industry in the Spanish economy. Spain was in the World top three ranking by international tourist arrivals and by income in 2015. The development of the tourism industry is essential to maintain the established economic system. However, if the environmental requirements were not taken into account, the country would face a negative effect on depletion of local environmental resources from which tourism depends. This case study evaluates, through a life cycle perspective, the average carbon footprint of an overnight stay in a Spanish coastland hotel by analyzing 14 two-to-five-stars hotels. Inventory and impact data are analyzed and presented both for resource use and greenhouse gases emissions, with the intention of helping in the environmental decision-making process. The main identified potential hotspots are electricity and fuels consumption (6 to 30 kWh/overnight stay and 24 to 127 MJ/overnight stay respectively), which are proportional to the number of stars and unoccupancy rate and they produce more than 75% of the impact. It is also revealed that voluntary implementation of environmental monitoring systems (like EMAS regulation) promotes collection of more detailed and accurate data, which helps in a more efficient use of resources. A literature review on LCA and tourism is also discussed. Spanish hotels inventory data presented here for the first time will be useful for tourism related managers (destination managers, policy makers and hotel managers among others) to calculate sustainability key indicators, which can lead to achieve real sustainable-tourism goals. Further data collection will be needed in future projects to gather representative data from more hotels, other accommodation facilities and also other products/services offered by tourist sector in Spain (like transport of tourists, food and beverage, culture-sports & recreation and others)

Using Life Cycle Thinking and Assessment for industrial waste management policy making

Postprint (published version