Compatible solid polymer electrolyte based on methyl cellulose for energy storage application: structural, electrical, and electrochemical properties

Compatible green polymer electrolytes based on methyl cellulose (MC) were prepared for energy storage electrochemical double-layer capacitor (EDLC) application. X-ray diffraction (XRD) was conducted for structural investigation. The reduction in the intensity of crystalline peaks of MC upon the addition of sodium iodide (NaI) salt discloses the growth of the amorphous area in solid polymer electrolytes (SPEs). Impedance plots show that the uppermost conducting electrolyte had a smaller bulk resistance. The highest attained direct current DC conductivity was 3.01 × 10−3 S/cm for the sample integrated with 50 wt.% of NaI. The dielectric analysis suggests that samples in this study showed non-Debye behavior. The electron transference number was found to be lower than the ion transference number, thus it can be concluded that ions are the primary charge carriers in the MC–NaI system. The addition of a relatively high concentration of salt into the MC matrix changed the ion transfer number from 0.75 to 0.93. From linear sweep voltammetry (LSV), the green polymer electrolyte in this work was actually stable up to 1.7 V. The consequence of the cyclic voltammetry (CV) plot suggests that the nature of charge storage at the electrode–electrolyte interfaces is a non-Faradaic process and specific capacitance is subjective by scan rates. The relatively high capacitance of 94.7 F/g at a sweep rate of 10 mV/s was achieved for EDLC assembly containing a MC–NaI system

Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%

A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell

The selection process of a suitable machine tool among the increased number of alternatives has been an important issue for manufacturing companies for years. This is because the improper selection of a machine tool may cause many problems that will affect the overall performance. Inthis paper, a decision support system (DSS) is presented to select the best alternative machine using a hybrid approach of fuzzy analytic hierarchy process (fuzzy AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE). A MATLAB- based fuzzy AHP is used to determine the weights of the criteria and it is called program for PriorityWeights of the Evaluation Criteria (PWEC), and the PROMETHEE method is applied for the final ranking. The proposed model is structured to select the most suitable computer numerical controlled (CNC) turning centre machine for a flexible manufacturing cell (FMC) among the alternatives which are assigned from a database (DB) created for this purpose. A numerical example is presented to show the applicability of the model. It is concluded that the proposed model has the capability of dealing with a wide range of desired criteria and to select any type of machine tool required for building an FMC

Experimental Investigation of Mechanical Properties of PVC Polymer under Different Heating and Cooling Conditions

Due to a widely increasing usage of polymers in various industrial applications, there should be a continuous need in doing research investigations for better understanding of their properties. These applications require the usage of the polymer in different working environments subjecting the material to various temperature ranges. In this paper, an experimental investigation of mechanical properties of polyvinyl chloride (PVC) polymer under heating and cooling conditions is presented. For this purpose standard samples are prepared and tested in laboratory using universal material testing apparatus. The samples are tested under different conditions including the room temperature environment, cooling in a refrigerator, and heating at different heating temperatures. It is observed that the strength of the tested samples decreases with the increasing of heating temperature and accordingly the material becomes softer. Meanwhile the cooling environments give a clear increasing to the strength of the material