Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric

Simultaneous spectrophotometric determination of Co (II) and Co (III) in acidic medium with partial least squares regression and artificial neural networks

This study aims at the application of two chemometric techniques to visible spectra of acetic acid solutions of Co (II) and Co (III) for simultaneous determination thereof. Spectral data of 145 samples in the range of 400-700 nm were used to build the models. Partial least squares regression models were developed for which latent variables were determined using internal cross-validation with a leave-one-out strategy and 3 and 2 latent variables were selected for Co(II) and Co(III) based on root mean square error of cross-validation. For these models, root mean square errors of prediction were 1.16 and 0.536 mM and coefficients of determination were 0.975 and 0.892 for Co (II) and Co (III). As an alternate method, artificial neural networks consisting of three layers, with 10 neurons in hidden layer, were trained to model spectra and concentrations of cobalt species. Levenberg-Marquardt algorithm with feed-forward back-propagation learning resulted root mean square errors of prediction of 0.316 and 0.346 mM for Co (II) and Co (III) respectively and coefficients of determination were 0.996 and 0.988.

Mobilisation of textile waste to recover high added value products and energy for the transition to circular economy

The textile industry is a major contributor to global waste, with millions of tons of textiles being discarded annually. Material and energy recovery within circular economy offer sustainable solutions to this problem by extending the life cycle of textiles through repurposing, recycling, and upcycling. These initiatives not only reduce waste but also contribute to the reduction of the demand for virgin materials (i.e. cotton, wool), ultimately benefiting the environment and society. The circular economy approach, which aims to recreate environmental, economic, and societal value, is based on three key principles: waste reduction, material circulation, and ecological restoration. Given these difficulties, circularity incorporates the material recovery approach, which is focused on the conversion of waste into secondary raw resources. The goal of this notion is to extract more value from resources by prolonging final disposal as long as feasible. When a textile has outlived its functional life, material recovery is critical for returning the included materials or energy into the manufacturing cycle. The aim of this paper is to examine the material and energy recovery options of main raw materials used in the fashion industry while highlighting the need of close observation of the relation between circularity and material recovery, including the investigation of barriers to the transition towards a truly circular fashion industry. The final results refer to the main barriers of circular economy transition within the industry and a framework is proposed. These insights are useful for academia, engineers, policy makers and other key stakeholders for the clear understanding of the industry from within and highlight beyond circular economy targets, SDGs interactions with energy and material recovery of textile waste (SDG 7, SDG 11, SDG 12 etc.).

Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach

Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R²). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R² values that could be achieved, reaching 0.9989.