Quaid-e-Awam University of Engineering, Science and Technology

The Split Source Inverter (SSI) is a single-stage DC-AC converter topology widely recognized for its beneficial features compared to the Z-source inverter. These advantages include a reduced number of components, a constant input current, and minimized losses. This study aims to extensively investigate and analyze the operation of a 3 kW Linear Induction Motor (LIM) using SSI through comprehensive simulations. The suggested control approach, employing finite control-model predictive thrust control (FC-MPTC), exhibits remarkable dynamic behavior and achieves a fast transient response without necessitating modifications to the control loop. Moreover, the proposed method allows for the manipulation of multiple variables by utilizing a single cost function, eliminating the need for lookup tables. The efficacy of the proposed control method is validated through comprehensive simulations and experimental tests, which exhibit accurate reference tracking speed and superior dynamic performance in regulating the thrust and primary flux. The results also showed that the proposed method had lower undulations of 5.8% than the traditional method. The proposed approach outperforms the DTC-SVM method under varying load and speed conditions by delivering remarkable accuracy and improved control characteristics.

Globally there is already a lot of pressure on water resources because of climate change, economic development, as well as an increasing global populace. Many rivers originate in the mountains, where snowfall fluctuations and the global climate’s inherent unpredictability affect the hydrological processes. Climate change sensitivity has been recognized in recent years and would affect hydropower, such as humidity, cloudiness, and precipitation, that are considered; global warming emerges as one of the most important contributors to climate change. The Yangtze River supports rich biodiversity and provides important ecosystem services for human survival and development. In addition, climate changes, particularly short-term and long-term precipitation and temperature fluctuations, influence the snow regime and the hydrological development of river flow response at the basin and sub-basin scales. More precise this review focused to understand the hydropower potential, freshwater fisheries, and hydrological response of snow dynamics in snow-dominated basins.

In the modern day, oil and gas companies must meet fresh problems to effectively maximize their performance, not only about traditional performance such as reliability or productivity, but also emerging ones, related to sustainability issues. The authors present the development of a novel and robust surge prediction and energy performance control technique with the use of precise anti surge control, from the measurement to the control algorithm to the anti-surge valve and load variation with the support of speed control to ensure reliability and mitigate power loss caused by anti-surge valve opening by operating the centrifugal gas compressor adjacent surge control line. In the methodology the design and original equipment manufacturer (OEM) data for the compressor were reviewed, and then the off-design operating envelope was examined. Aspen HYSYS version 12.1 is used for dynamic simulation modelling. According to the first stage findings with 10% surge safety, the load variation control approach is more energy-efficient than compressor operation at maximum load, which consumes 13596 kWh with 99–94 MMSCFD during peak and off-peak demand and uses less energy 5183 kWh for 99–67.59 MMSCFD compression. Surging occurred when the flow rate decreased from 7022 to 4355 m3/h for variable speed and from 7022 to 5990 m3/h for fixed speed. When a surge was likely to occur, ASV opened aggressively at 22.29% with variable speed and 11.77% with fixed speed. Based on the results of the second stage with 10% safety, the load variation control method is more energy efficient, consuming between 23,288 and 9892 kWh with a 217 and 178MMSCFD compressor capacity, as opposed to the compressor running at maximum load, which consumes 23288 kWh with a 217–234 MMSCFD during peak and off-peak demand. It resulted in surging when the flow rate fell from 4167 to 2967 m3/h for variable speed and 4167–3906 m3/h for fixed speed. When a surge was about to occur, ASV opened aggressively at 14.79% with variable speed and 10.08% with fixed speed.

This study aims to numerically investigate the impact of air supply terminal devices (ASTDs) on the performance of novel variable refrigerant flow (VRF) integrated stratum ventilation (SV) system. The novel VRF-SV hybrid system was designed and analyzed in the authors’ previous work, where different design configurations were tested and their performances were compared. However, the effect of ASTDs on the performance of novel system has not yet been thoroughly explored. Therefore, this study was designed to investigate the effect of four types of ASTDs, including bar grille, double deflection grille, drum louver and jet slot diffusers installed in the laboratory and meeting room environments. The temperature and velocity field distribution, thermal comfort and indoor air quality (IAQ) were examined as performance indicators. The Re-Normalization Group (RNG), Realizable and Standard k-ϵ turbulence models were also employed in this study to verify and validate the numerical predictions with the experimental measurements. The experimental results found relatively close to the predictions obtained through RNG k-ϵ model with maximum deviations at any location were found to be 0.28 m/s in terms of velocity and 1.6°C in terms of temperature magnitude. Further research demonstrated that among the studied ASTDs, the bar grille diffuser provided an adequate thermal environment for the occupants with an acceptable jet throw and IAQ. This benefit also suggests that if the bar grille is installed with the VRF-SV system, the system would require less fan power, and thus, it reduces the energy use by the fan.

The rapid advancement of artificial intelligence (AI) technologies has led to a transformation in higher education worldwide. AI tools provide academic support to students anywhere and anytime to enhance their knowledge and skills. Those facing difficulties have been relying on traditional support and guidance. However, this support has experienced difficulties, including availability and accessibility. This study examines the potential of AI-powered tools to address these challenges, aiming to make academic support more accessible, efficient, and effective. This study focuses on understanding the determinants of AI tools' acceptance and use for academic support among students, influencing student satisfaction and academic performance in Pakistan and Malaysia. The research on AI tool acceptance and use in the higher education Institutions (HEI) context is still new and less explored in Pakistani and Malaysian higher education institutions. A theoretical model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and other factors was employed to identify factors that affect AI tool adoption in higher education. The survey research design was employed, and the total sample size was 305 respondents, with 203 students from Quaid-e-Awam University of Science and Technology (QUEST), Pakistan, and 102 students from Universiti Teknologi Malaysia (UTM). A “Partial least squares structural equation modeling (PLS-SEM) Analysis” was employed to assess the research model and hypotheses using SmartPls 4.0. In Pakistan and Malaysia, students are more concerned about using AI tools to improve their academic performance. The findings indicated that performance and effort expectancy, information accuracy of AI tools, pedagogical fit to meet the student’s expectations, and student interaction with tools were important factors in predicting the acceptance and use of AI tools among students of both countries in higher education, and the rising use of these AI tools has improved students’ satisfaction levels and significantly impacted students learning outcomes in both countries. Additionally, student engagement and personal innovativeness have not significantly affected the use of AI tools among students in both countries. This study provides a comprehensive analysis of AI tool adoption in the unique contexts of Pakistan and Malaysia, contributing to the broader discourse on technology integration in higher education.

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

Herein, Zinc Oxide (ZnO) Nanoellipsoids (ELs) were grown on a paper substrate using template-free solution based low temperature method. The evolution of as per grown ZnO Els was recorded at different intervals of time using scanning electron microscopy (SEM). Furthermore, the structural and optical properties were investigated by high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS) and photoluminescence (PL). As results of this study, it is presumed that the morphology of an individual ZnO EL may be composed of numerous numbers of ZnO nanorods with hexagonal structure elongated along the c-axis direction. Every individual NR underwent an evolution process at the middle of NR which served as a secondary nucleation site for the growth of more NRs and gradually with respect to time an ellipsoidal architecture is formed. Overall, it was found that the formation of ellipsoids is constituted from an integrated assembly of the nanowires and ZnO NRs provided secondary nucleation sites for the formation process. This phenomenon is unreported by the previous studies and needs further research to be conducted.

Faulty compressors must be timely detected to prevent excessive energy consumption, maintenance, and energy costs. Existing diagnostics models lack addressing the energy performance indicators and do not provide effective hybrid machine learning (ML) model for advanced fault diagnosis to prevent compressors from becoming energy hogs. Therefore, this study proposed a novel approach in the form of an energy-based diagnostic model for integrating energy performance indicators to detect the healthy and faulty behavior of the compressor using a hybrid ML model. The time series analysis and Isolation Forest techniques have been used to detect faulty and healthy behavior of centrifugal gas compressor. To obtain more insight and prevent false alarms the hybrid ML model was introduced. The Ridge regression was used as the meta-classifier in the suggested hybrid model, which receives the input from the base classifiers Decision Tree (DT), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) to optimize the performance and accuracy of hybrid model. This study was conducted on a two-stage centrifugal compressor that compresses production gas for export powered by a gas turbine at Malaysia’s PETRONAS Angsi oil and gas field. According to the findings, the energy efficiency predicted for the first stage was 84.2% for healthy behavior and 69.7% for faulty behavior, while for the second stage, it was 83.2% for healthy behavior and 68.1% for faulty behavior, indicating high energy efficiency during the healthy operation of a centrifugal compressor in comparison with faulty behavior. The slight difference between the proposed diagnostic model training, testing, and prediction performance accuracy 0.98, 0.97 and 0.99 proposes a model is efficient neither overfitting nor underfitting according to the value of co-efficient of determination (R2). The R2 values for training, testing, and prediction performance accuracy for the GB model were 0.95, 0.93, and 0.94; for kNN, 0.89, 0.87, and 0.86, and for Tree, 0.95, 0.94, and 0.93 respectively. According to the results, the proposed hybrid model performs more eloquently and efficiently than other single models DT, kNN, and GB. This study empowers operators to take critical measures to increase energy efficiency, reduce downtime, and schedule maintenance to improve the reliability of centrifugal gas compressor.

Water pollution represents a formidable environmental concern, a direct consequence of burgeoning industrial and economic activities. Human engagements, encompassing industrial, agricultural, and technological endeavors, have exacerbated the proliferation of environmental pollutants, thereby engendering deleterious repercussions upon both the ecosystem and public health. Of particular concern is the infusion of heavy metals into the wastewater stream, primarily stemming from their use in textile dye production. The global ubiquity of heavy metals as pollutants underscores the grave threat they pose to human health and ecological well-being, primarily attributable to rapid urbanization and industrial expansion. In response to this exigency, the scientific community has concentrated its efforts on devising efficacious water treatment methodologies, which encompass techniques such as adsorption, precipitation, and filtration. Among these techniques, adsorption emerges as an uncomplicated, efficient, and cost-effective mechanism for the removal of heavy metals from water. Aerogels have surfaced as a promising adsorbent material, chiefly due to their attributes, including low density, high porosity, expansive surface area, limited thermal and electrical conductivity, and the capability to respond to external stimuli. This review comprehensively elucidates the application of silica-based and nanocellulose-based aerogels for the adsorption of heavy metals. It places a distinct emphasis on recent and pioneering advancements within these aerogel categories, where researchers have realized augmented adsorption capacities through tailored process modifications. Finally, this review offers insights into the gaps and challenges pertaining to synthesis and metal adsorption by silica-based and nanocellulose-based aerogels that need to be addressed in the future research.

The present research involves the selection of suitable edible and non-edible feedstocks for the production of biodiesel in Pakistan. After studying and analyzing different feedstocks: Jatropha, Neem and waste cooking oil is selected due to their availability and low free fatty acid contents which is almost less than 30%, for techno-economic analysis. The transesterification process is applied for the conversion of these edible and non-edible feedstocks into biodiesel. Three projects have been chosen in which 1000L biodiesel can be generated. According to data obtained from different resources, the initial investment of Neem, Jatropha and waste cooking oil projects are $14,183, $13,883, and $12,183. After calculating the initial project investment, different capital investment techniques are calculated such as internal rate of return, profitability index and payback period. Feasibility analysis is done that shows, which project, should be adopted according to prevailing circumstances. The project with a short payback period is chosen for investment. Therefore, by doing techno-economic analysis of these three oils for the production of biodiesel and after that compare it with the petrol-diesel, one can enhance the research capability and productivity of biodiesel in Pakistan.

All species on this planet, both living and non-living, require water. It is well known that the availability of clean water sources is dwindling and that the rapid development of industry and technology has increased the number of hazardous effluents released into the environment. Before being released into the environment, industrial, agricultural, and municipal wastewater must be treated to remove dangerous contaminants such as organic colours, pharmaceutical wastes, inorganic compounds, and heavy metal ions. They pose major threats to human health and can pollute our environment if not controlled. Membrane filtration is a tried-and-true technique for removing germs and numerous hazardous substances from water. Carbon nanoparticles are used in wastewater treatment because of the promising surface area of sorbents. With the growth of nanotechnology, carbon nanomaterials (CNM) are being created and used in membrane filtration (MF) for effluent treatment before being terminated. To remove wastewater contaminants, this paper investigates using CNMs such as fullerenes, graphene’s, and CNTs. By examining sorption rate, selectivity, permeability, antimicrobial disinfectant properties, and environmental compatibility, we concentrate on these CNM-based membranes and this approach due to its attributes and utilization and how they can improve the performance of the frequently used membrane filtration system.

In authentic circumstances, this qualitative case study explores vocabulary acquisition tactics used by English as Foreign Language (EFL) learners. The study investigates several methods impacted by individual learning preferences, cultural contexts, and exposure to real-world language use through in-depth interviews. The research is guided by two main goals: determining techniques in real-world settings and comprehending learners' assessments of their efficacy. The context of the study is Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah. The research paradigm chosen for this study was Qualitative. Population of the study was from the Department of English and Purposive Sampling was used. The results provide valuable perspectives for language education, highlighting the significance of context, interaction, and tailored methods in the growth of vocabulary. Our understanding of language acquisition dynamics is intended to be enhanced, and the study contributes to efficient language learning approaches by providing insights into instructional practices, curriculum design, and learner support measures.

Communication skills are the most important skills that students need in their academic as well as professional fields. Students, especially those in the English department, may be involved in various activities such as presentations, group discussions, and debates, and soon they need to master communication skills to effectively perform these tasks. This study aims to investigate the communication skills of the third-year students of the English department at Quaid e Awam University Nawabshah. Here, four communication skills, namely reading skills, speaking skills, listening skills, and writing skills, are analyzed using descriptive statistics through an online 4-point Likert scale questionnaire. Data were collected from 34 respondents, based on four dimensions: reading skills, listening skills, writing skills, and speaking skills. The site of the study was Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah. The findings of the study reveal insightful outcomes. Based on the outcomes of the study, it is suggested that the study can be replicated in the context of schools which the acquisition of these four skills is mandatory for high academic achievements.

This research was conducted on a comprehensive psychoanalytic inquiry of Alex Michaelides' novel 'The Silent Patient,' focusing on unraveling the intricacies of silence and trauma within the narrative. The study meticulously analyzes the protagonist Alicia Berenson's deliberate choice of silence as a profound form of communication, transcending simplistic victimhood narratives. Employing a qualitative approach, the research delves into the psychological landscape of the characters, with a specific focus on the evolution of Alicia's character. The socio-cultural and historical contexts, particularly within a psychiatric institution, are examined to understand the external factors shaping the characters' experiences. Through thematic exploration, the study identifies recurring motifs such as suppressed personal narratives and the transformative potential of self-discovery. This research contributes to the discourse on mental health representation, emphasizing the enduring relevance of psychoanalytic theory in illuminating the complexities of the human psyche within literary exploration.

The study discusses the variation in general (everyday) communication found in the Urdu lexicon in the previous 10 years. The research is qualitative, based on random interviews collected from Urdu speakers belonging to District Sanghar and Karachi from Sindh; and Lahore from Punjab. The study is based on the data provided by the interviewed participants who have rich experience and proficiency in Urdu. The article aims to find the changes in the lexicon of Urdu that include modifications in Urdu vocabulary by borrowing words and code-mix in communication. It discusses the causes of variations (technology, migration, and language imperialism) in detail and determines the status of language after these changes. It highlights the words used in Urdu speech whose equivalent alternatives are present in Urdu and those words that have died due to a lack of use of those words. It determines the impact of geography, modernization, globalization, and cultural coherence on language. It studies the changes in words used to call family members and friends and the changes in speech for communication between friends and family.

In buildings, air conditioning and mechanical ventilation (ACMV) systems are the major shareholders of overall energy consumption. Energy-efficient designs for ACMV systems in building applications are therefore needed. While designing an efficient ACMV system, consideration must be given to the growing concerns of enhanced thermal comfort and improved indoor air quality. The variable refrigerant flow (VRF) air-conditioning system is a widely adopted alternative to the existing building cooling systems due to the higher energy efficiency and individualized temperature control feature. However, it still suffers from shortcomings such as no outdoor air induction for ventilation and higher initial cost. Therefore, this paper reviewed the variable refrigerant flow and mechanical ventilation/air distribution systems, their integrated designs for non-residential buildings, performance evaluation and control optimization of the integrated systems, VRF systems’ faults detection and diagnosis, current application of the VRF systems, and associated challenges. Together with these all, some advanced buildings’ cooling techniques and improvements toward nearly/net-zero energy buildings are briefly discussed. Indoor thermal comfort models and criteria for different climates are also presented for an in-depth understanding of the VRF integrated mechanical ventilation designs. The literature survey shows that the supply air temperature and airflow rate are foremost in parameters that can be optimized in VRF integrated ventilation design as they greatly reduce the energy consumption. Further, policies on elevated indoor temperatures in air-conditioned buildings to mitigate their carbon footprint are strictly being implemented. Therefore, this review provides an insight to the researchers for further improvement in the integrated design and control optimization of the parameters involved. A paradigm shifts from the conventional compression-based electric-powered air conditioning systems to the renewable energy driven advanced air conditioning technologies which is also an emerging research area to be focused on achieving the target of nearly/net-zero energy buildings.

While extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss.