Identifying breach mechanism during air-gap spinning of lignin–cellulose ionic-liquid solutions

To be able to produce highly oriented and strong fibers from polymer solutions, a high elongational rate during the fiber-forming process is necessary. In the air-gap spinning process, a high elongational rate is realized by employing a high draw ratio, the ratio between take-up and extrusion velocity. Air-gap spinning of lignin–cellulose ionic-liquid solutions renders fibers that are promising to use as carbon fiber precursors. To further improve their mechanical properties, the polymer orientation should be maximized. However, achieving high draw ratios is limited by spinning instabilities that occur at high elongational rates. The aim of this experimental study is to understand the link between solution properties and the critical draw ratio during air-gap spinning. A maximum critical draw ratio with respect to temperature is found. Two mechanisms that limit the critical draw ratio are proposed, cohesive breach and draw resonance, the latter identified from high-speed videos. The two mechanisms clearly correlate with different temperature regions. The results from this work are not only of value for future work within the studied system but also for the design of air-gap spinning processes in general

Specification and prediction of nickel mobilization using artificial intelligence methods

Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO 4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment. © Versita Sp. z o.o

Improved RMR rock mass classification using artificial intelligence algorithms

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures