International Journal of Advance Industrial Engineering
International Journal of Advance Industrial Engineering is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of industrial. engineering.The goal of this journal is to provide a platform for engineers and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of industrial engineering. INPRESSCO is dedicated to present state of-the-art, high quality, research developments in all areas of industrial engineering research, including applications in industry and services, to a broad audience of academics and professionals. Target areas include manufacturing, operations management, product and process design, information systems, business and strategy, and quality management amongst others. Important fields of industry: Food Technology: Food chemistry& nutrition ,Food microbiology, Food engineering ,Principles of food processing, Food Packaging technology, Food quality systems & Management, Techniques in food analysis, Technology of fruits and vegetables processing, Technology of cereals and oilseeds, Bakery and Confectionary technology ,Technology of plantation spices ,Technology of milk and milk products, Snack foods Technology ,Beverages Technology ,Food Biotechnology, Food Additives and Ingredients ,Enzymes in food processing ,Food physics ,Food Toxicology ,Principles of Human Nutrition ,Food Preservation, Industrial Fermentation, Food Safety and Hygiene Leather Technology: Pretanning operation, tanning operations, posttanning and finishing operation, footwear technology. Leather Apparel and Goads technology, tilization of animal byproducts and byproducts of meat industry. Fertilizers. Gelatin and glue manufacture from collagen of tannery byproducts. Collagen stability and collagen cross links. New application for animal hides and skins, eco-friendly processes. Textile Technology: Fibre science and technology, yarn engineering, fabric engineering chemical processing. Fibers Properties, Waste and Pollution Control in Textile Industry Pulp and paper: Wood Chemistry, Pulp Raw Materials, Pulping and Bleaching, Bio-pulping Processes, Colloidal Chemistry and Paper Making, Coating and Printing Technology, Deinking and Paper recycling Technology, Forest and Pulp mill Management, Blending Technologies in Paper Manufacture, Quality control in pulp and paper industry Materials Science and Engineering :Mineral Binder, Building Materials, Metallurgy, Composite Materials Corrosion, Plastic and rubber technology, polymer Miscellaneous Field: Cement, glass and ceramics industries and white ware, Oil and soap, Detergents industries, Dyes and pigments, Environmental pollution and control in field of industry, Petrochemical and petroleum refining
Strategic drivers of digital transformation in small and medium enterprises
This study explores the strategic drivers influencing digital transformation within small and medium enterprises (SMEs). The purpose of the research is to identify the key external and internal factors that motivate SMEs to adopt digital technologies and reconfigure their business processes in response to changing market dynamics. A systematic review methodology was employed, analyzing existing academic literature, industry reports, and empirical studies to synthesize current knowledge on the subject. The findings reveal that competitive pressure, evolving customer expectations, operational efficiency goals, technological advancements, and regulatory requirements serve as major catalysts for digital transformation in SMEs. The review further highlights that innovation capacity and organizational agility play critical roles in accelerating adoption and sustaining transformation outcomes. The study concludes that digital transformation in SMEs is not driven by a single factor but rather by an interplay of strategic motivations shaped by both market forces and internal organizational priorities. These insights contribute to a clearer understanding of digital transformation pathways and offer implications for SME policymakers, managers, and future research.
Digital Transformation Trends in Small and Medium Enterprises: A Systematic Review
Digital transformation has become a critical strategic priority for small and medium enterprises (SMEs) seeking to enhance competitiveness, resilience, and long-term sustainability in an increasingly digital economy. The purpose of this study is to systematically review and synthesize existing academic literature on digital transformation trends in SMEs, with a focus on adopted technologies, key drivers, barriers, and organizational outcomes. This research adopts a systematic literature review (SLR) methodology, following established review protocols to identify, screen, and analyze peer-reviewed journal articles published in major academic databases. Relevant studies were selected based on predefined inclusion and exclusion criteria, and qualitative content analysis was used to categorize themes and research patterns. The findings reveal that SMEs primarily adopt digital technologies such as cloud computing, e-commerce platforms, data analytics, and Industry 4.0 solutions to improve operational efficiency and market reach. Leadership commitment, digital skills, and external partnerships emerge as key enablers, while limited financial resources, skill shortages, and resistance to change remain significant barriers. Overall, digital transformation is associated with improved performance, innovation capacity, and organizational resilience. In conclusion, the review demonstrates that digital transformation in SMEs is a multifaceted and evolving process that extends beyond technology adoption to include strategic, organizational, and cultural dimensions. The study highlights existing research gaps and offers directions for future research on long-term impacts, contextual differences, and sustainable digital transformation strategies in SMEs.
Securing enterprise LLM gateways & misconfigured proxy exposure detection + mitigation framework for exposed LLM endpoints, API gateways, model download hooks
The quick process of enterprise adoption of large language models (LLMs) via API gateways, reverse proxies, and orchestration layers has enabled a novel category of security threats related to architecture misconfigured as opposed to model-specific behavior. Open LLM endpoints, permissive proxy settings, and unprotected model download handles are facilitating more and more data leakage, unauthorized inference, supply-chain failure and trust boundary breaches. This paper describes a system level detection and mitigation system framework to secure enterprise LLCM gateways based on the co-design principles of cyber-physical systems, embedded security, and safety-critical architectures. The framework is both based on semantic configuration analysis and runtime monitoring, and secure-by-design architecture patterns to highlight early exposure detection and design-time mitigation of the gateway. The paper shows how the knowledge of cross-domain automotive, IoT, and CPS security can be used to design resilient LLM infrastructure to enable scalable, trustworthy, and governable enterprise AI deployments.
Huge Language Models; API Gateways; Proxy Misconfiguration; AI Infrastructure Security; System-Level Co-Design; Supply Chain security; Enterprise AI Governance
Copyright (c) 2026 International Journal of Advance Industrial Engineering
Explainable Artificial Intelligence for Protecting the U.S. Financial System from Sanctions Evasion and Trade-Based Money Laundering
The integrity of the U.S. financial system is increasingly threatened by sophisticated sanctions evasion networks and trade-based money laundering schemes that exploit cross-border payment channels and global supply chains. While machine learning techniques have enhanced the detection capabilities of anti-money laundering and sanctions screening systems, their limited transparency poses significant challenges for regulatory compliance, supervisory review, and effective financial intelligence generation. This study proposes an explainable artificial intelligence framework for protecting the U.S. financial system by enabling detection models that not only identify illicit financial risk but also clearly articulate the underlying drivers of that risk. The research develops explainable AI-based anti-money laundering models tailored to U.S.-touch payment and trade finance environments, including cross-border wire transfers, correspondent banking flows, and ISO 20022-aligned messaging. The framework targets two priority threat domains identified in U.S. national-security guidance: sanctions evasion involving restricted jurisdictions and proxy counterparties, and trade-based money laundering linked to supply-chain manipulation, over-invoicing, and under-invoicing practices. Explainability mechanisms are integrated at the feature, counterparty, transaction routing, and documentation levels to generate investigator-readable narratives and examiner-ready audit trails. Empirical results demonstrate that the incorporation of explainable artificial intelligence significantly reduces false-positive alert volumes and shortens investigation time compared to traditional opaque models, while improving the clarity and consistency of suspicious activity reporting. By enhancing transparency, accountability, and operational efficiency, the proposed approach strengthens regulatory trust and supports higher-quality financial intelligence outcomes. These findings underscore the role of explainable artificial intelligence as a critical enabler of effective anti-money laundering enforcement, sanctions compliance, and national-security protection within the U.S. financial system.
Machine Learning Algorithms for Enhancing Predictive Analytics in ERP-Enabled Online Retail Platform
Enterprise Resource Planning (ERP) systems are known to be key to online retail management and its resources and operations where proper demand forecasting and sales projections are needed to be effective and competitive. This paper presents a Convolutional Neural Network (CNN) based prediction model for online retail systems that are ERP-enabled. Online Retail II is used to test the model. The methodology starts with the large-scale data preprocessing that involve cleaning, feature engineering, label encoding, and normalization with the help of Standard Scaler and then the further division into testing and training sets entails. The following methods are used to evaluate and train convolutional neural network (CNN) models: root-mean-squared error (RMSE), R2-score, mean absolute percentage error (MAPE), and mean absolute error (MAE). With an R2 of 94, MAE of 2.277, RMSE of 2.814, and MAPE of 13.72%, the experimental findings show that the suggested CNN outperforms the conventional machine learning models. Additional comparative analysis indicates the superiority of the CNN over the models of Decision Tree, Gradient Boosting and Random Forest, which prove its strength in reflecting complex transaction patterns. The results highlight the opportunities of deep learning in enhancing online retail forecasting using ERP which, in turn, enhances business decision-making, operational effectiveness, and customer satisfaction.
Predictive Analytics, ERP, Retail Analytics, Machine Learning, Automation, Optimization, Predictive Analytics, Business Intelligence, Sales.
AI-Driven Customer Relationship Management: Enhancing Salesforce Efficiency Through Predictive Analytics
The use of artificial intelligence (AI) with Customer Relationship Management (CRM) systems has transformed the way organizations operate their sales pipelines, anticipate customer behaviour, and manage the effectiveness of their workforce. This paper looks at the place of AI-based CRM solutions, specifically focusing on predictive analytics as a driver of Salesforce efficiency. Lack of data silos, manual reporting, and inability to utilize forecasting have long been a problem with traditional CRM models. By contrast, AI-powered tools make use of machine learning algorithms, natural language processing, and real-time data to automate the process of scoring leads, enhance sales projections, and customize customer engagement strategies. Based on new evidence regarding the industry, such benefits as enhanced conversion rates, increased retention, and decreased operation costs are mentioned in the discussion. Nonetheless, there are still difficulties, such as the issue of data privacy, the ethical aspect of algorithmic decisions, and compatibility with older systems. The paper also examines the future prospects, including hyper-personalization, conversational AI, and prescriptive analytics, under the overall digital transformation agenda. The study highlights the strategic significance of ensuring that predictive technologies are aligned to human-centric values by critically assessing opportunities and risks in order to optimize the business performance and customer trust. Finally, the analysis shows that predictive AI is not a replacement of human judgment but a complementary tool that can enable organizations to be able to foresee needs, build stronger relationships and maintain a competitive advantage in the rapidly changing digital market.
AI-driven CRM, predictive analytics, Salesforce efficiency, customer engagement, sales forecasting, digital transformation.
AI-Driven Agriculture Marketplaces Digitalizing Farm to Retail Supply Chains in UK
The fast digitization of the agriculture industry is changing the dynamics of the farm-to-retail supply chain, and Artificial Intelligence (AI) has become a driver of efficiency, visibility in the process and sustainability. This paper investigates the emergence of AI-driven agricultural marketplaces in the United Kingdom and how predictive analytics, dynamic pricing, automated quality scoring and intelligent logistics are changing agrifood traditional value chains. Using recent case studies, regulatory considerations, and the regulatory drivers of adoption, the researchers detail how online platforms increase the connectivity between farmers and retailers and help them to avoid food wastage, better price realizations, and create resilience when faced with supply shocks. The most significant disadvantages, including data governance, interoperability, shortage of digital skills, and trust-related issues are also highlighted. The paper makes contributions to the current debates about the digitalisation of agriculture by putting forward a UK-specific lens to the topic of the integration of AI-enabled marketplaces into farm-to-retail supply chains, providing implications to policymakers, practitioners, and researchers.
AI agriculture, digital marketplaces, farm-to-retail supply chains, UK agrifood sector, predictive analytics, traceability, sustainability.
Revitalizing Agriculture in Conflict Zones: A Case Study on Machinery Restoration in Borno State
Agricultural productivity in northeast Nigeria has been drastically affected by armed conflict and Borno State ranks among the worst-affected states. Lack of farmland, mislocation of agricultural populations and destruction or damages of farm equipment(farm machinery) were some of the major results seen after years of violence, which is very important machinery in the big agricultural system. The paper examines how the restoration of machinery can be used to give new life to agriculture in conflict-struck lands by studying Borno State. With the use of qualitative field interviews, secondary data, and policy reports the paper focuses on how public, private and international stakeholders have helped in rebuilding agricultural equipment, and enabling accessibility of mechanized tools by the farmers. Results indicate that selective machinery repair schemes, equipment sharing co-operates, and homegrown repair training have helped to make farms productive and communities stronger. Nonetheless, the issues of insecurity that have lingered on, lousy infrastructure, and retention of spare parts has impeded sustainable recovery. The research highlights why it is so necessary to embed machine restoration into the larger agricultural and post-conflict development plans. It provides policy consideration in expanding an intervention to assist in the process of mechanization, livelihood recovery, and long-term food security in weak environments.
Agricultural recovery; Conflict zones; Borno State; Machinery restoration; Post-conflict development; Mechanization; Food security; Nigeria; Agricultural resilience.
Leveraging Six Sigma for Continuous Improvement in Petroleum and Chemical Processing
The petroleum and chemical processing industries operate in highly complex, capital-intensive environments where operational efficiency, safety, and environmental compliance are paramount. In such settings, continuous improvement methodologies have become essential to maintain competitiveness and sustainability. This article explores the strategic application of Six Sigma, a data-driven quality management approach originally developed for manufacturing in the context of petroleum refining and chemical production. Drawing from industry case studies, literature reviews, and operational data, the article evaluates how Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) framework can be effectively leveraged to reduce process variability, enhance yield, minimize waste, and drive cost savings. It highlights real-world implementations, such as catalyst optimization, emissions control, and downtime reduction, that demonstrate measurable improvements in performance metrics. The paper also critically examines the challenges of adopting Six Sigma in traditionally conservative engineering environments, including organizational resistance, training demands, and integration with legacy systems. Finally, the article discusses broader industry and policy implications, advocating for targeted capacity-building programs, regulatory incentives, and public-private collaborations to embed continuous improvement practices across the sector. By positioning Six Sigma as a catalyst for operational excellence and innovation, the article contributes to the evolving discourse on quality and sustainability in energy-intensive industries.
Six Sigma, Continuous Improvement, Petroleum Industry, Chemical Processing, Operational Excellence, DMAIC, Process Optimization, Quality Management, Industrial Efficiency, Sustainability, Lean Six Sigma.
Optimizing Fluid Flow and Pressure Control in Industrial Operations: A Case Study
Solid control of flow and pressure of fluids is core to efficiency, safety and cost effectiveness of industrial processes within industries like petrochemicals, manufacturing and energy. This paper represents an intensive case study of a medium-sized industrial facility and comes up with resolving inefficiencies in the flow management system and pressure control. The work examines possible approaches to the optimization of fluid transport based on computational fluid dynamics (CFD) simulations, real-time deployment of sensors and system reconfiguration, in order to mitigate system losses. As the results show, the actual improvements in flow-rate stability and energy consumption by flow are impressive, even when insignificant design alterations are made like valve repositioning, changes in diameter of pipes, refined integration of control logic. The figures after the implementation depict that pressure fluctuations have dropped by 22 percent and there is a 15 percent increase in the uptake of pump energy consumption. Another issue presented in the paper is the practicality of the economic and operational aspects of upgrading the current systems with automation technology so that they may be smarter in controlling the pressure. The results can also be used to contribute to the upcoming body of knowledge of this topic, that is, process optimization, and also provide some practical recommendations to engineering professionals who want to optimize the performance of a system without investing a significant amount of capital.
Fluid dynamics, Pressure control, Industrial optimization, Computational fluid dynamics (CFD), Flow regulation, Process engineering.
Advanced Simulation on Techniques for Predicting Gas Behavior in LNG and NGL Operations
When it comes to Liquefied Natural Gas (LNG) and Natural Gas Liquids (NGL) processing plants, the precise forecast of gas behaviour is a key component in the enhancement of the safety, efficiency and reliability of plant operation. Tightly coupled with the large dynamism of thermodynamic properties of natural gas mixtures at cryogenic temperatures and pressures, the standard methods of estimating will not always serve the interests of natural gas mixtures at high pressures and cryogenic temperatures, as they do not capture the nature of complexities in phase nature, composition, and flow dynamics. This paper discusses various highly detailed simulation methods that have become prominent in LNG and NGL practices, which take such extensive modeling like thermodynamic modeling, molecular modeling, and compound process simulators like Aspen HYSYS, and OLGA. The importance of choosing and using suitable equations of the state, the use of dynamic simulation to make predictions in real-time and the inclusion of simulation with a control system to be able to respond toward operations is stressed. The industrial feasibility of the various methods and tradeoffs in computation are also reviewed. Results indicate that the effective use of simulation tools can not only help increase the accuracy of prediction but also achieve proactive decision making in complex conditions of gas processing. This marks the research, the necessity of further development of the simulation structure to meet the changing needs of the operation, varying gas mixture.
LNG, NGL, Gas behavior, Simulation techniques, Thermodynamic modeling, Process optimization, Thermodynamic modeling, Equations of state, Dynamic simulation.
Photocatalytic degradation of methylene blue dye using synthesized CoxNi1-xFe2O4nanoparticlesunder direct visible light
A cobalt doped nickel ferrites were prepared in this study. Photo catalysts made of cobalt-doped nickel ferromagnetic materials nanoparticles were utilized to degrade dyes. These nanoparticles were synthesized by the co-precipitation technique. A UV-Vis spectrophotometer was used to determine the band gap. These ferrites had band gap between 2.32eV to 2.20eV. The spinel structure of ferrites was characterized X-ray diffraction (XRD) technique. The shape and average size of synthesized nanoparticles was determined by scanning electron microscopy (SEM). Fourier transform infrared FTIR shows metal-oxygen bond in the range of 400-1000cm-1. The photocatalytic degradation of dye under UV radiation and direct sunlight was studied. Almost 72 to 92 percent methylene blue dye was degraded in first 100 mints owing to the active octahedral to tetrahedral lattice sites and prevent the recombination of electron/hole pair. The photo-degradation of methylene blue (MB) and a few textile dyes was practised to examine the photocatalytic performance of Co-NiFe2O4. It was observed as the most effective photocatalyst for treating sewage.
Cobalt chloride hexahydrate; Nickel chloride hexahydrate; Spinel ferrites; Methyl blue dye; Photocatalytic activity; Electron/hole pair recombination mechanism
Department of Physics, Government College University Faisalabad, Pakistan
This research was conducted to study acoustic noise isolation performance of coco peat and recycled rubber composite material, which has high potential to be used for sound absorbing material. The sample was tested with different composition ratios. The composition ratios varied from 0% to 100 % of coco peat and recycled rubber in the steps of 20%. The samples were manufactured by using mold that was made by using MS sheet with the 5mm thickness. These samples were tested by using DEWE-43 instrument used for finding the sound absorption coefficients. Similarly, simulation was conducted on Ansys software for finding the sound absorption coefficient. For composition 40% of coco peat & 60% of recycled rubber at the various frequencies such as 250,500,750 and 1000 Hz at constant sound pressure source of 100db, the sound absorption coefficient levels achieved were 43.7db, 10.2db, 9.7db and 0.3db respectively for DEWE-43 instruments. These results are better as compared to Baggas panels. These results indicate that manufactured composite panels are promising to be used as sound absorbing material with low cost, light weight, and biodegradable.
Sound Absorption; Composite Material; Recycled Rubber.
The synthesis of metal oxide nanomaterials for energy storage and optimization of their electrochemical performance is vital for next-generation energy solutions. This study focused on synthesizing nickel hydroxide nanomaterials using precipitation and microwave methods for use as pseudo-capacitor electrodes. The precipitation method, utilizing starch as a capping agent, produced nickel hydroxide with a maximum specific capacitance of 333 Fg⁻¹. Calcination of nickel hydroxide at elevated temperatures yielded nickel oxide/carbon (NiO/C). The electrochemical performance of NiO/C was optimized at calcination temperatures of 400, 500, and 600 °C, with the 500 °C sample achieving a maximum specific capacitance of 450 Fg⁻¹ at a scan rate of 5 mV·s⁻¹. Structural characterization was performed using X-ray diffraction (XRD), while Fourier-transform infrared spectroscopy (FTIR) investigated functional groups. Morphological analysis was conducted via field emission scanning electron microscopy (FESEM), and elemental composition was assessed using energy dispersive spectroscopy (EDS). Electrochemical properties were evaluated through cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS).
Nickel hydroxide; Electrochemical performance; Nanomaterials; Precipitation method
Construction site safety and quality analysis utilizing KHNN Model
Improving construction site safety through effective hazard identification and mitigation is critical. This study aims to predict rework, defects, and associated costs using artificial neural networks and optimization algorithms. Traditional safety planning approaches lack pre-construction hazard analysis. To examine deficiencies, various metrics were analyzed, including rework costs per $1M scope and injury rates. Ineffective safety practices like inadequate training and protection have led to accidents. This work identifies approaches to enhance worker safety performance through hazard identification. Inputs to a neural network model predict rework workers, defects, and costs. Safety execution aims to systematically identify hazards before construction. Model performance using actual data was evaluated. Two soft computing methods - artificial neural network and optimization algorithms - were implemented in MATLAB. Krill herd and grey wolf optimization techniques optimized hidden neuron weights in the neural network structure. Predictions from these algorithms outperformed other existing methods like particle swarm and genetic algorithms. This study provides a framework to quantitatively forecast rework, defects, and associated costs through systematic pre-construction hazard analysis and modeling. The proposed optimizationenhanced neural network models can help construction managers implement targeted safety improvements.
Krill Herd Neural Network, Particle Swarm Optimization, Grey Wolf Optimization, Work Health and Safety, Artificial Neural Network
Effect of magnetic field on germination and early growth of soybean (Glycine max) seed.
Magnetic field is beneficial and essential tool in modern research. Magnetic field is a force that can be made by moving electric charges and magnetic dipoles which apply a power on other close-by moving charges and magnetic dipoles. Magnetic field has numerous applications in our daily life. Nowadays it is used in agriculture to enhance the seed germination, plant growth and yield because it has no toxic effect on environment as need us on soil. In this research work effect of magnetic field on soybean seed (Glycine max) was investigated. The soybean seed was taken from institute of Horticulture science. Non treated seeds were served as control while treated seeds were served as magnetically treated seeds. Seeds before sowing were treated with magnetic field stimulator which is fabricated in department of Physics University of agriculture Faisalabad, by varying magnetic field for different time exposure. The seed was exposed for different time duration and different magnetic field strength. The seeds were grown in three replicates in Petri dishes as well as in pots. The different parameters like germination, seedling growth, seedling fresh weight, seedling dry weight, root length, shoot length, root dry weight, shoot dry weight, leaf area, enzymes activity, proteins content, chlorophyll content and antioxidant activities were measured. It was noticed that the magnetic field effected the seed germination and other parameters. Difference between treated and non-treated plant seeds was checked by using different statistical tests namely RCBD, CRD, and ANOVA.
Magnetic field, Glycine max, RCBD, CRD, ANOVA
Recycling textile industry waste materials: fabrication, characterization, mechanical and thermal performance analysis of developed non-woven interlining
In the last few years, the shortage of natural resources, the energy crisis and environmental issues have threatened the world simultaneously. Furthermore, the manufacturing sector is facing the most vulnerable economic conditions due to high raw material, energy and operating costs. It is critical to emphasize cost-cutting without sacrificing sustainability. Herein, it has been attempted to recycle textile waste material into non-woven interlining. The fabrication process includes the collection of textile waste materials, bleaching the fabric, garneting to loosen fibrous and conversion to nonwoven web form through an aqueous solution of fibrous flock with a mixture of epichlorohydrin as a cross-linker. Three nonwoven interlinings were successfully developed from 100% cotton, 50% polyester/50% cotton and 75% polyester/25% cotton blended ratio of waste fabric. Linear weight, thickness, tear strength, tensile strength, Eb%, bursting strength and TGA of these samples are assessed. Furthermore, we compare the recycled sample cost and investigate the seam performance of developed samples made from various woven twill fabrics. It is observed that the developed samples show satisfactory performance and are cheaper than the current market price, having potential scope in clothing manufacturing as well as offering a sustainable approach to the environment.
Climate change, recycling, waste materials, textile industry, non-woven interlining, TGA, seam efficiency
Experimental Investigation of Thermal Fatigue life of Engine Cylinder Liner
Thermo mechanical stresses play an important role in the failure of critical parts of many applications like turbines, power plants, hot rolling mills and especially in automotive industry. Thermal fatigue is the major cause of this damage of metals due to thermal cycling. In automotive industry thermal fatigue occurs in engines due to variation in temperature during intake and exhaust stroke. This gradual thermal cycling damage the engine parts especially engine cylinder liner which reduces the service life of an engine. So, it is very important to estimate the thermal fatigue life of engine cylinder liner material. In this research work, thermal fatigue life of engine cylinder liner material grey cast iron has been investigated. A wedge-shaped specimen is prepared from sand casting and used for the experimentation of thermal fatigue. Thermal fatigue experiments are performed between 750C2800C thermal cyclic ranges. Crack initiation and propagation are investigated experimentally on one edge of wedge shape specimen and validate of these propagations of cracks with numerical simulation using ABAQUSTM software.
Thermal fatigue, thermal stress, mechanical stress, crack initiation, crack propagation, crack saturation
Comparison of Numerical Techniques for the solutions of Nonlinear Singular Differential Equations
In this research work, the approximate solutions for nonlinear singular initial value problems are to be calculated by using modified Laplace decomposition (MLDM). The modified Adomian decomposition method and Homotopy perturbation methods are to be used to calculate the approximate solutions for the same problem. Moreover, the convergence analysis and the error found for the approximate solution are to be discussed. To prove the robustness and effectiveness of the proposed method, several examples are to be considered and the results calculated in this way will be compared with those obtained from other two above mentioned method. The time consuming behaviour of these method will be noted in order to check the simplicity, accuracy and efficiency of method
Initial Value Problems, Boundary Value Problems, Modified Laplace Decomposition Method, Homotopy Perturbation Method. He,s polynomials.
A comprehensive review on the use of marble waste in concrete
Researchers in the construction sector have recently explored replacing cement and/or natural aggregates used in manufacturing cement-based materials with non-biodegradable waste, such as marble dust. Numerous studies have investigated the impact of using marble dust in concrete due to its availability and the significant environmental issues it poses. Previous research has primarily focused on using marble dust as a replacement for cement, fine aggregate, or coarse aggregate. This review examines the use of dimensional stone waste from the extraction and processing of marble as a component of concrete. The effects of substituting marble debris for conventional concrete constituents on the mechanical, fresh, and durability properties were investigated. The findings suggest that blending marble debris with fly ash before using it as a binder is more advantageous than simply replacing Portland cement. Additionally, waste marble can be used to substitute fine and coarse aggregates in larger quantities while still maintaining acceptable results. The performance depends on the specific fine aggregate being replaced and the particle size distribution of the coarse aggregate's geological origin. The review concludes that waste marble can adequately replace coarse/fine aggregates, cement, and admixture materials in concrete when used at specific ratios, resulting in higher-strength concrete.
Cement, Natural aggregates, Coarse/fine aggregate, Marble waste
Electrical power plays a key role in the modern world. With growing technological inventions and our reliability on the electrical energy, it is now considered as the inevitable need. From complex industrial machinery to data centers around the globe, the economies are thriving on electrical power. Although there has been a lot of development on issues related to delivering the power to the end consumers, the critical question i.e. the quality of power still has many areas of concern which are needed to be addressed. With the introduction of a myriad of high-end tech equipment, sophisticated computers, and electronic devices, the power quality can easily jeopardize the functionality and reliability and can put this equipment at risk which can cause a considerable loss in terms of both financial and economic aspects. This paper discusses the power quality improvement by introducing the Dynamic Voltage Restorers on the distribution end of the power system. DVRs can produce active and reactive power. With properly controlled mechanisms, this generated power can be injected into the system to overcome the disturbances and ensure the quality of the power supply. The DVRs addresses the two major areas of power quality. Voltage sag and the voltage swell. The selection of DVRs is primarily based on easy-to-implement and cost-effective solutions as compared to the other industrial solutions. The paper will analyze the effect of the DVRs on the power quality by using a prototype DVR model based on a 1kVA load connected to a simulated distribution feeder. DR program for energy management.
Power Quality, Voltage Sag, Dynamic Voltage Restorers.
Wood Modification by Fluorocarbons of different chain lengths
Wood has useful characteristics due to which it has many uses and applications in particular in engineering and over all in every spheres of human life. But wood has also a lot of draw backs that limits its applications in many areas. One of the main is that wood is hygroscopic and has great tendency to absorb water and moisture due to major hydrophilic components of wood. The main aim of our research work is to modify the hydrophilic nature of yellow pine wood by blocking or decreasing (OH) groups inside the wood by using fluorocarbons of different chain lengths (CF4, CF6 ,CF8.). These are very promising materials frequently used in textile sector to impart hydrophobicity in fibers. Main focus is by using different fluorocarbons to explore the potential of different chain lengths for wood modification by improving its hydrophobicity, thermal properties, fireret ardency, color protection and dimensional stability. In practice, different fluorocarbons were vacuum impregnated into the pine wood to reduce the density of (OH) groups in its molecular structure. Subsequently, the treated wood samples were characterized by Scanning electron microscope (SEM) for its surface morphology and surface activation and contact angle measurements were used to reveal the degree of super hydrophobicity of fluorocarbon modified wood. Water related properties like water and moisture sorption and ant swelling efficiency and fire resistance properties were also extensively tested. Finally test properties were optimized and correlations were established among the treatments parameters and improvements achieved by using fluorocarbons of different chain lengths. Since, fluorocarbon based chemical wood modification is low cost, innovative approach to suppress the shortcoming of normal wood in a non-toxic manner and has huge potential to find applications in furniture and construction sectors in near future.
Hydrophilic nature, fluorocarbons, Scanning electron microscope, super hydrophobicity
Quebracho - Oxazolidine Combination Tanning for Leather Making
Different markets require the manufacture of chrome-free leather to have comparable properties to chrome tanned leather such as feel, fullness, softness, and hydrothermal stability. The deep interest in clean technologies and stringent norms set by regulations; have led the tanners to increase their efforts to develop chrome free tanning agents. Combination tannages are considered as suitable alternatives for a chrome-free tanning system. Among the innumerable combination tannages that are currently being exploited are vegetable tannin/oxazolidine combinations as the most promising options. In this study, organic combination tanning process based on quebracho powder (Schinopsis balansae) and oxazolidine for the production of upper leathers is presented. Quebracho powder has been utilized in the combination tanning system with oxazolidine. Quebracho tanned leathers from goat skins used as control leathers. It has been observed that Quebracho - oxazolidine combination tanning which employs 20% Quebracho and 5% oxazolidine provides a shrinkage temperature of 102oC, which is 18oC more than the control leathers. The characteristics of the leathers indicate that the Quebracho -oxazolidine combination system provides leathers with good organoleptic properties and comparable strength properties. The experimental tanning system provides significant reduction in the discharge of total dissolved solids in the wastewater. The leathers have been further characterized for chemical analysis. The leathers obtained from the combination system are lighter in color compared to control leathers. The manufacture of upper leathers using combination of Quebracho and oxazolidine suggest that this tannage is a promising alternative to traditional chrome tanning.
Combination tannage, Quebracho, Oxazolidine, Chrome free
Development of a Strategy for Economic Classification of Spare Parts in Maintenance Based on Criticality
The effective maintenance will minimize the chance of failure of spare parts and thereby maximize overall profit of operations, which is required for economic manufacturing. The study has strategically classified spare parts in related to the inventory cost(s) utilizing schedule maintenance period, economic order quantity, optimum period of supply and optimum number order. The model developed to forecast for spare parts requirement in the automobile maintenance industry has made use of simple exponential smoothing method. The reliability of critical parts and failure pattern were considered for the formulation of the generalized spare parts inventory model, under negative exponential distribution. The model was tested with data collected on D6c manual in a Maintenance Organization. ABC analysis was used to analyze the data from which the most critical parts (called class A) were identified which served as input into the model. Thereafter, the model was numerically analyzed using linear multiple regression method. The finding generally shows that the cost of critical specific spare part varies with maintenance scheduling time and quantity of order, number of order, and time of order. Besides, the study also shows that it is profitable to schedule maintenance annually than lesser periods.
maintenance, failure, spare parts, criticality, inventory, strategy
Thermal Performance of Different Types of Engineering Materials in Basra City
The present work is aimed to reduce the annual electric energy consumption in a residential building in Basra city through introducing a standardized rule for the annual electrical consumption for the cooling and heating purposes. This work will concentrate on all parameters which help to go toward the optimum use of thermally efficient house. The building energy analysis program HAP 4.9 was used to simulate the annual energy consumption for a typical residential house built with six types of building materials. Building systems included a conventional building material (common brick) as base case, three types of common insulations (polyurethane, polystyrene and Cellulose fiber insulation), a flat type of Insulating Concrete Form (ICF) wall, and an Autoclaved Aerated Concrete (AAC) block wall. This research showed alternating significance between two different building techniques utilized in the study in terms of space cooling, heating and total annual energy consumption. These building techniques are (AAC and ICF) with liquid insulation coating which showed respectively (32.1%) and (26.83%) energy savings compared with the base case. Also, this study demonstrated the significance of the building techniques which utilize (window shading by overhangs and building orientation) where (33.45%) and (34.6%) respectively energy savings was shown for the AAC building system compared with the base case.
AAC, ICF, polyurethane, polystyrene, Cellulose fiber insulation
Kinetics and mathematical modeling of thin layer drying of mango leather
Mango is a major producing fruit in Bangladesh. According to the research, post-harvest loss of mango is about 34% of the total production. To reduce huge losses, leather production by drying is a good alternative. In this paper, the thin layer drying kinetics of formulated mango leather were studied at different temperatures (55, 60 and 65°C) and thicknesses (3mm and 5 mm). Drying time was decreased with increasing temperature and increased with increasing leather thickness. Seven different models (Lewis, Henderson and Pabis, Page, Modified Page, Logarithmic, Wang and Singh and Midilli et al.) were used to describe drying characteristics where all models were compared with coefficient of determination ( ), reduced mean square of deviation ( ), mean bias error (MBE), root mean square error (RSME) and standard error of estimate (SEE). The Midilli et al. model was found to be best for describing thin layer drying of mango leather. According to Fick’s diffusion model, the effective moisture diffusivities were between 2.17× to 3.45× /s. The activation energy of moisture diffusion was estimated to be 41.8141 kJ/mol.
Mango leather, Drying kinetics, Thin layer drying models, Moisture diffusivity, Activation energy
Development of an Alternative Hybrid Power System using Hybrid Micro Power Optimization Model (HOMER)
Nigeria is endowed with abundant energy resources, both conventional and renewable, which can potentially provide the country with a sufficient capacity to meet the ambitions of both urban and rural Nigerians of a full, nationwide electrification level. Yet, Nigeria has one of the lowest consumption rates of electricity per capita in Africa. With the demand superseding the generation, there is inequitable access of rural communities to the electricity service in the country. There are inherent obstacles militating against the effective implementation of an orderly energy policy in Nigeria. The inefficiencies overshadowing the allocation of energy resources coupled with the near depletion of fossil fuels, make it imperative for the country to exploit its huge natural renewable resources to avoid a worsening energy supply scenario and provide feasible electricity to rural dwellers. This project presents a review of renewable energy potentials in Nigeria to be tapped for useful and uninterrupted electric energy supply. This study provides a detailed analysis of the quantum of electricity supply to Ilesha garage, Akure and how to supplement for lose hours in power supply (i.e. hours lost to power outage) via solar energy to augment for 18 hours of power unavailability in the community.
Hybrid Power System, Development, Optimization, Model, Micro Power
Assessment of Machining and Assembly Activities Performance in a Local Subsea Production System
The cost of running operations and executing project in developing country is high partly due to wastes that could be eliminated through effective implementation of quality tools. There are layers of wastes that increases time required to execute tasks from tender stage to contract acquisition. The study covered specific improvement initiatives implemented in a local Oil and Gas Project. It covered activities in fabrication and assembly plants in one of the under developed country. There were visits and interactions with staff of the organizations in the area where operational and project data was collected and also review their existing method in use. A data collection plan was developed in conjunction with checklist to guide the process. Both documents were designed so that the personnel responding to questions were able to identify specific issues encountered in their operations, the type of quality intervention applied and the results of the intervention. Quality review processes were initiated to resolve the problems identified. Root Cause Analysis (RCA) was performed by utilizing the “5 Why Analysis” Root Cause Method. The findings revealed that, there is significant relationship between quality intervention and project execution leading to an overall reduction in waste and thus improving compliance products and services, customer satisfaction and profitability. The involvement and commitment of top management is essential for the intervention to be successful. Hence, the study further reveals that the tools of Quality can be adapted to the Local environment and implemented. Finally, the study demonstrated that quality intervention can be used to solve the problems highlighted.
Quality, Waste, Fabrication, Assembly, Maintenance, Manufacturing
Machine Utilization Technique for Job Shop Scheduling using Tabu Search Algorithm
Efficiency in job shop scheduling plays an important role where a large number of jobs and machines are needed to be scheduled. As the number of jobs and machines increase, the job shop scheduling problems approach nondeterministic polynomial (NP) hard difficulty. It is one of the hardest combinatorial optimization problems for which it is extremely difficult to find optimal solutions. In the last two decades, many heuristic methods have been devised that produce solutions which are near optimal. Here, we present one such heuristic technique called Tabu search algorithm that can be applied to solve Job shop scheduling (JSS) problems. The purpose of this study is to generate different schedules with corresponding completion time using the Tabu search algorithm. The code for tabu search algorithm was done in python language. With the help of Gantt chart, it has found the percentage of machine utilization and idleness for all machines. At last, future directions have been provided for further improvement.
Job Shop Scheduling, Machine Utilization, Neighborhood, Tabu Search, Tabu List, Total Completion Time
Optimisation of Energy Content of Palm Kernel Shell (PKS) using Modelling Approach
Twenty samples of Palm Kernel Shells (PKS) (ranging from 0.1g to 2g in step of 0.1g) were prepared. The samples were subjected to ultimate and proximate analyses to determine the energy content using methods of Association of Official Analytical Chemist (A.O.A.C.). The PKS samples were heated in a locally made tubular furnace (oven) and the oven temperatures were consecutively maintained between 2000C and 3500C in step 300C. The twenty (20) experimental runs (based on number of samples) were carried out at every oven temperature step. The outcomes of the experiment showed that the Optimum Higher heating Value (HHV) of the PKS (6618.50) is predictable at 2600C (furnace temperature) over contents’ composition using Response Surface Analysis (RSA). The results indicated that the PKS can be optimally converted to heat energy under the stated experimental conditions.
PKS, A.O.A.C., HHV, energy content, regression model, optimisation, tubular furnace.