Papers by Keyword: PCA

Abstract: This study investigates the calco-carbonic balance of drinking water in Taza, Morocco, a critical parameter for ensuring water quality and preserving distribution infrastructure. Using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) with Statistica 12, we examine the interactions between the physicochemical parameters and the Langelier Saturation Index (LSI). The study highlights the difficulty of factors influencing this balance, which is crucial for preventing scaling. Scaling can lead to reduced water flow, decreased energy efficiency, increased maintenance costs, premature equipment wear, and deterioration of water quality. The results identify the parameters impacting this balance, including temperature, total hardness, dissolved oxygen, and pH. PCA enabled us to extract valuable insights from physicochemical analyses, revealing significant correlations between these parameters and suggesting optimization strategies.The predictive model for the Langelier Saturation Index, with a determination coefficient (R² = 0.925) and a standard error (σerr = 0.07), provides a valuable tool for expecting and correcting imbalances, therefore ensuring better management of drinking water quality in Taza.

Abstract: The most vulnerable food products related to halal issues are in the form of mixing beef and chicken meat with pork, which has a physical resemblance if not carefully considered. The rise of meat adulteration is often found due to high demand and high prices. For this reason, a fast, effective, and low-cost meat adulteration detection tool is needed. Detection of beef and chicken adulteration in this study was carried out using a VIS-NIR spectrophotometer from an AS7341 multispectral sensor equipped with an LED light source and 11 channels to read the reflection of meat light in the near light and near infrared ranges, raspberry pi as a microcontroller, data displayed on an LCD stored in CSV form. The results of sensor response patterns formed in beef, chicken, pork, mixed beef-pork, and minced chicken-pork mixed meat show different characteristics. Then to clarify the characteristics of each meat, the results of the sensor response were analyzed using the Principle Componen Analysis (PCA) method. The results of data reduction from PCA projections through Principle Component 1 and Principle Component 2 regions are able to detect the presence of pork mixture in beef and chicken. The results of the PCA score plot on beef, pork and cow-pig mixture the percentage of PC1 is 100% and PC2 is 0% while on chicken, pork and chicken-pig mixture the percentage of PC1 is 100% and PC2 is 0%. The results of this study show the great potential of using a portable spectrophotometer using the AS7341 sensor whose results are analyzed using the PCA method to detect adulteration of minced beef and chicken.

Abstract: In this study, multi objective optimization for improving the joining characteristics of dissimilar AA5083-AA6061 alloys during FSW has been presented. Tool rotational speed, feed and tilt angle are the input parameters whereas tensile strength and hardness are the responses. Experiments are planned and conducted as per Taguchi L₉ orthogonal array. Main effects plot and contour plot discloses the parametric influence over the responses. Hybrid GRA and PCA were deployed as tools to perform multi objective optimization. Results pointed out the fact that tilt angle played vital role in affecting the responses followed by feed and tool rotational speed. The optimum parametric settings obtained are speed 710 rpm, Feed 50 mm/rev, and Tilt angle 2^o.

Abstract: The main objective of this work is to apply the Principal Component Analysis (PCA) to the key parameters of a micromechanical model, namely the shape parameter of inclusion (grain) (ratio =a/b) and γ viscoplastic parameter in view of a better simulation. In this work, the sensitivity of the model to parameters and γ is evaluated on the stabilized global stress during cyclic Tension-Compression (TC) loadings and out-of-phase Tension–Torsion, with a sinusoidal waveform and a phase lag of 90 between the two sinusoidal signals TT90 loadings. Indeed several values ​​of and γ are pulled thanks to these loading, we use later the PCA in order to choose the couple (, γ) adequate to launch our simulation. The model used is expressed as part of the self-consistent approach and time-dependent plasticity. Based on the Eshelby tensor, this model considers that the elastic behavior is compressible. For a polycrystalline structure, the grains are deformed by crystallographic sliding located in the most favorably oriented systems and which support a strong constrained stress.

Abstract: The possibility of using renewable feedstocks for biodiesel production and reducing gas emissions makes it an attractive large-scale substitute to traditional fossil diesel. Although renewability is one of the main driving forces in biodiesel use, traditional production routes employ methanol as the transesterification agent, a chemical generated from fossil carbon. Aiming at further improving biodiesel’s sustainable performance, the replacement of methanol by ethanol has been proposed. Use of the ethylic production route could further reduce CO₂ emissions, energy consumption and generate more jobs. The objective of this study is to unveil whether substituting methanol for ethanol does indeed result in a less carbon and energy intensive production chain while also increasing job generation and decreasing social strife. To assess production chain performance a lifecycle approach was used composed by: (i) Data assemblage from literature to represent the ethylic/methylic biodiesel systems; (ii) Construction of quantitative indicators to compare material and energetic flows; and (iii) Principal Component Analysis (PCA) for data interpretation and relevance ranking of calculated social/environmental indicators. Focus was given to CO₂ emissions, energy consumption and social aspects of sustainability. Results show that use of ethanol does indeed reduce CO₂ emissions, due to extra agricultural carbon sinks in the production chain but increases energy consumption and energy loss. Methanol also resulted in a chain with higher average wages, more jobs generated and less forced labor cases but with a higher accident rate and a high salary disparity. PCA showed that carbon intensity is one of the most important environmental metrics while energy consumption was considered secondary, but the high correlation between these aspects highly impact chain sustainability. PCA also greatly differentiated agricultural and industrial links of respective production chains, with industrial links being governed by CO₂ emissions and process safety and agricultural links by water consumption, land use and energy loss. A distinct tradeoff was seen between environmental and social considerations of sustainability and between carbon intensity and energy consumption reductions. As a result, substitution is only justified in scenarios in which CO₂ emissions outweigh energy intensity and social aspects.

Abstract: In this paper, a guided wave temperature robust PCA-based stress monitoring methodology is proposed. It is based on the analysis of the longitudinal guided wave propagating along the path under stress. Slight changes in the wave are detected by means of PCA via statistical T² and Q indices. Experimental and numerical simulations of the guided wave propagating in material under different temperatures have shown significant variations in the amplitude and the velocity of the wave. This condition can jeopardize the discrimination of the different stress scenarios detected by the PCA indices. Thus, it is proposed a methodology based on an extended knowledge base, composed by a PCA statistical model for different discrete temperatures to produce a robust classification of stress states under variable environmental conditions. Experimental results have shown a good agreement between the predicted scenarios and the real ones

Abstract: Principal components analysis (PCA) is a data analysis and reduction technique widely used in the method of statistical analysis,elucidation of the variation of five mechanical properties (tensile strength, bending strength, impact strength, tensile modulus and bending modulus) and five microstructure properties (carbonyl index, hydroxyl index, branching degree, chain scission degree and unsaturated value) that taken place when subjected to ultraviolet (UV) aging of high density polyethylene (HDPE) used as rotational packaging case by using PCA. The information of main components were extracted and studied. Also the relationships between performance properties were considered. Finally, the combined evaluating parameter Z was established and analyzed. The results show that the first and second components extracted by PCA can be determined on behalf of all components. Among the 10 performance parameters, tensile strength, bending strength and impact strength are strongly correlated, carbonyl index, hydroxyl index, branching degree and chain scission degree are strongly correlated, and the tensile modulus is non-correlated to other performance parameters. The trendy of the evaluating parameter Z show that the HDPE aged quickly in the first 20 days and leveled off in the following days.

Abstract: We investigated multi objective optimization on age hardenable aluminum alloys (AA2024 and AA6061) using Taguchi based grey relational analysis (TGRA). We considered three significant process parameters such as rotational speed, welding speed, and axial force and evaluated the welding responses such as ultimate tensile strength and tensile elongation of fabricated weld specimens. Principal component analysis (PCA) was applied to estimate accurate weighting quality characteristics of each response in TGRA. Optimized process parameters were 1700rpm rotational speed, 60mm/min welding speed and 6kN axial force. Results of this approach will be useful in controlling the process parameters effectively.

Abstract: Mould risk is an increasing problem in current housing branch. Mould is considered to be one of the most important features of Sick Building Syndrome. In most cases it is caused by the increased moisture of building barriers and improper humidity of indoor air. In old buildings it is caused by improper raising techniques, lack of isolation against moisture and insufficient building materials applied for construction. Modern housing also suffers problem of mould risk which is connected to introducing of the new materials and technologies for external envelopes of the buildings. These often increase the tightness of the buildings and cause improper performance of natural ventilation systems, which makes suitable conditions for mould to grow.In the paper there is proposed an attempt to evaluate mould risk in the buildings using e-nose, being a gas sensors array which consists of eight metal oxide semiconductor (MOS) gas sensors. This device is commonly applied for air quality assessment in environmental research. First part of the article is a description of e-nose technology and its possible applications in constructions. The second part shows the exemplary e-nose readouts of indoor air sampled in clean reference rooms and threatened with mould development. Obtained multivariate data are processed and visualized using a Principal Component Analysis (PCA).

Abstract: It is essential to learn a robot navigation environment. We describe research outcomes for KSU-IMR mapping and intelligence. This is for navigating and robot behavior learning. The mobile maps learning and intelligence was based on hybrid paradigms and AI functionaries. Intelligence was based on ANN-PCA for dimensionality reduction, and Neuro-Fuzzy architecture.