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Subject: search.filters.subject.Principal component analysis (PCA)

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    Modelling pollutant washoff from south-east queensland catchments Australia
    (International Water Association (IWA), 2003) Goonetilleke, Ashantha
    Onsite wastewater treatment systems aim to assimilate domestic effluent into the environment. Unfortunately failure of such systems is common and inadequate effluent treatment can have serious environmental implications. The capacity of a particular soil to treat wastewater will change over time. The physical properties influence the rate of effluent movement through the soil and its chemical properties dictate the ability to renovate effluent. A research project was undertaken to determine the role that physical and chemical soil properties play in predicting the long-term behaviour of soil under effluent irrigation and to determine if they have a potential function as early indicators of adverse effects of effluent irrigation on treatment sustainability. Principal Component Analysis (PCA) and Cluster Analysis grouped the soils independently of their soil classifications and allowed us to distinguish the most suitable soils for sustainable long term effluent irrigation and determine the most influential soil parameters to characterise them. Multivariate analysis allowed a clear distinction between soils based on the cation exchange capacities. This in turn correlated well with the soil mineralogy. Mixed mineralogy soils in particular sodium or magnesium dominant soils are the most susceptible to dispersion under effluent irrigation. The soil Exchangeable Sodium Percentage (ESP) was identified as a crucial parameter and was highly correlated with percentage clay, electrical conductivity, exchangeable sodium, exchangeable magnesium and low Ca:Mg ratios (less than 0.5).
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    Framework for soil suitability evaluation for sewage effluent renovation
    (Springer, 2004-04) Goonetilleke, Ashantha
    Current methods of establishing suitable locations for onsite wastewater treatment systems (OWTS) are inadequate, particularly in light of the numerous cases of onsite system failure and the resulting adverse consequences. The development of a soil suitability framework for assessing soil suitability for OWTS allows a more practical means of assessment. The use of multivariate statistical analysis techniques, including Principal Component Analysis (PCA) and multi-criteria decision aids of PROMETHEE and GAIA, enabled the identification of suitable soils for effluent renovation. The outcome of the multivariate analysis, together with soil permeability and drainage characteristics permitted the establishment of a framework for assessing soil suitability based on three main soil functions: (1) the ability of the soil to provide suitable effluent renovation, (2) the permeability of the soil, and (3) the soil’s drainage characteristics. The developed framework was subsequently applied to the research area, Gold Coast, Queensland, Australia, and the use of standard scoring functions were utilised to provide a scoring system to signify which soils were more suitable for effluent renovation processes. From the assessment, it was found that Chromosol and Kurosol soils provided the highest level of effluent renovation, closely followed by Ferrosol and Dermosol, Kandosol and Rudosol soil types. Tennosol and Podosol soil types were found to have a significantly lower suitability, with Hydrosol soils proving the least suitable for renovating effluent from OWTS.
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    ICT diffusion, trade openness and growth: empirical evidence from asian countries
    (Sage, 2024-08) Giri, Arun Kumar
    The present study contributes to the existing literature of trade-growth and information and communication technology (ICT)-growth nexus by examining the impact of ICT-trade nexus on economic and inclusive growth in 17 developing Asian countries for the time period 2005–2019. Using system generalised method of moments (GMM), the study confirms whether ICT diffusion enhances or distorts the impact of trade on growth. ICT diffusion is measured through a composite index of ICT (mobile, telephone, broadband, internet) constructed using principal component analysis (PCA) along with trade (per cent of GDP), GDP per capita and human development index (HDI) as a proxy for trade openness, economic growth and inclusive growth, respectively. Findings confirm positive impact of ICT diffusion and trade openness on both economic growth and inclusive growth. Thus, trade openness and ICT can be outlined as essential contributors to the growth of the major emerging Asian economies. The study concludes with policy implications focusing on investment in ICT sector and human capital development, which will consequently foster trade and uplift growth.
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    Identification of success clusters using principal component analysis for oil and gas industry
    (Taylor & Francis, 2025-02) Kakadea, Vijay
    Project success factors are key elements that contribute to the successful completion of any mega project. As project success factors vary from one industry to another, it is essential to tailor the success factors specific to a particular industry type. The oil and gas industry-specific success factors provide information to investing agencies of the steps to be undertaken to make the project a success. These success factors for the complete life cycle of the project offers a unique opportunity to the management in planning manpower and resources to meet the desired objectives. This study identifies 66 success factors for large oil and gas projects. A questionnaire survey was carried out to establish the most important factors or critical success factors/success clusters. One hundred forty-two responses were received from various stakeholders. Principal component analysis (PCA) was carried out to identify the clusters of success factors. This study recommends that for successful project implementation of a mega project, stakeholders should strive to focus on the eight key clusters identified by PCA. These eight key clusters may be utilized in similar mega projects such as chemical process industry, mining and metallurgical process industry and mega infrastructure projects.
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    A comparative assessment of Composite Environmental Sustainability Index for emerging economies: a multidimensional approach
    (Emerald, 2023-07) Mohapatra, Geetilaxmi
    The study finds that the overall CESI values lies between 2 and 4.8 for the 20 emerging countries considered in the study. This study depicts a diverse picture of environmental sustainability among emerging countries. The study also shows the trend of CESI values from 1990 to 2020. The bottom three countries whose CESI is very low compared to others are Iran, South Africa and Saudi Arabia. However, Brazil, Columbia and Chile are top three highest scorers in 2020.
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    Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1)
    (Springer, 2023-07) Pani, Ajaya Kumar
    In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment process (WWTP) ensures increased efficiency and effluents meeting stringent emission norms. Benchmark simulation model No. 1 (BSM1) provides a simulation platform to researchers for developing efficient data-based process monitoring, quality monitoring, and process control systems for WWTPs. The present article presents a review of all research works reporting applications of various machine learning techniques for sensor and process fault detection of BSM1. The review focuses on process monitoring of biological wastewater treatment process, which uses a series of aerobic and anaerobic reactions followed by secondary settling process. Detailed information on various parameters monitored, different machine learning techniques explored, and results obtained by different researchers are presented in tabular and graphical format. In the review, it was observed that principal component analysis (PCA) and its variants account for the maximum number of research works for process monitoring in WWTPs and there are very few applications of recently developed deep learning techniques. Following the review and analysis, various future scopes of research (such as techniques yet to be explored or improvement of results for a particular fault) are also presented. These information will assist prospective researchers working on BSM1 to take forward the research.
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    An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes
    (IOP, 2024) Pani, Ajaya Kumar
    Principal component analysis (PCA) and independent component analysis (ICA), as well as their kernel extensions, have been widely applied in the past for industrial fault detection with Gaussian or non-Gaussian process data with linear or non-linear characteristics. Kernel-based techniques lead to computational complexity due to the high dimensionality of the dataset in the feature space. In this work, a randomization approach is used to obtain a low-rank approximation of the high-dimensional kernel matrix. A hybrid machine learning technique is proposed that integrates randomized kernel PCA (RKPCA) with ICA and Gaussian mixture modeling (GMM). The proposed approach, ICA-RKPCA-GMM, addresses the Gaussian and non-Gaussian characteristics of non-linear process data. Another hybrid algorithm combining three basic techniques of ICA, PCA and GMM is also developed (ICA-PCA-GMM). The fault detection performances of the proposed techniques (ICA-RKPCA-GMM and ICA-PCA-GMM) are compared with PCA, ICA, KPCA and combined ICA-PCA techniques by applying the techniques to two benchmark systems. Monitoring performances were evaluated by determining the false alarm rate and fault detection rate for different types of process and sensor faults. The simulation results show that the proposed ICA-RKPCA-GMM approach yields better results than individual ICA, PCA and KPCA techniques, the combined ICA-PCA and the proposed ICA-PCA-GMM technique.
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    Measurement of antioxidant synergy between phenolic bioactives in traditional food combinations (legume/non-legume/fruit) of (semi) arid regions: insights into the development of sustainable functional foods
    (Springer, 2024-02) Sharma, Pankaj Kumar; Deepa, P.R.
    Numerous under-researched edible plants are present in the desert regions of the world. These plants could be potential candidates to ensure food security and provide valuable bioactive compounds through diet. In general, the bioactives present in food manifest synergistic, additive, or antagonistic interactions. The current study investigates such interactions between food combinations traditionally consumed in (semi) arid regions. Five edible plants (representing three food categories) were selected: Prosopis cineraria and Acacia senegal (legume), Capparis decidua and Cordia dichotoma (non-legume), and Mangifera indica (fruit), in which the first four are largely underutilized. The antioxidant capacities of individual plant extracts and their binary mixtures were analyzed by DPPH free radical scavenging and FRAP assays. The total phenolic content (TPC) and total flavonoid content (TFC) were also determined. The highest antioxidant activity was obtained for Prosopis cineraria extract (EC50—1.24 ± 0.02 mg/ml, FRAP value—380.58 ± 11.17 μM/g), while Mangifera indica exhibited the lowest antioxidant activity (EC50—2.54 ± 0.05 mg/ml, FRAP value—48.91 ± 4.34 μM/g). Binary mixture of Prosopis cineraria (legume) and Mangifera indica (fruit) manifested maximum synergy (experimental EC50—0.89 ± 0.01 mg/ml, theoretical EC50—3.79 ± 0.05 mg/ml). Correlation studies [Pearson’s correlation coefficient (r) and Principal component analysis (PCA)] showed a high correlation of TFC with DPPH and TPC with FRAP values. LC–MS analysis of methanolic plant extracts detected 43 phenolic compounds (including phenolic acids, flavonoids, and isoflavonoids), possibly responsible for the observed food synergy. For edible plants of the (semi) arid zones, this study is a first-of-its-kind and provides scientific validation to the traditional wisdom of consuming these foods together. Such indigenous food combinations derived from desert flora could offer valuable insights into development of sustainable functional foods and nutraceuticals.
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    Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining
    (Elsevier, 2014-11) Sangwan, Kuldip Singh; Garg, Girish Kant
    Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time.
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    Adaptive Multivariate Data Compression in Smart Metering Internet of Things
    (IEEE, 2021-02) Tripathi, Sharda
    Recent advances in electric metering infrastructure have given rise to the generation of gigantic chunks of data. Transmission of all of these data certainly poses a significant challenge in bandwidth and storage constrained Internet of Things (IoT), where smart meters act as sensors. In this work, a novel multivariate data compression scheme is proposed for smart metering IoT. The proposed algorithm exploits the cross correlation between different variables sensed by smart meters to reduce the dimension of data. Subsequently, sparsity in each of the decorrelated streams is utilized for temporal compression. To examine the quality of compression, the multivariate data is characterized using multivariate normal-autoregressive integrated moving average modeling before compression as well as after reconstruction of the compressed data. Our performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver. The proposed algorithm is tested in a real smart metering setup and its time complexity is also analyzed.