Engineering Science & Technology

Evaluation of Key Performance Factors and Recommendation of Optimization Strategies of a Power Generation Company

Wed, 13 Nov 2024 00:00:00 +0800

This paper presents the analysis of key performance indicators and some effective improvement strategies of four gas turbine generators (GTG) of Nigeria's power plant company for four years (2019-2022). The investigation used the NERC/IEEE Standard 762 (2006) generator performance indices amongst other calculated key performance indices to evaluate the collected data. The research methodology was done through the collection of data using questionnaires, operational records, and plant data sheets recorded by operators in the power station and data analysis using Excel software, and then constructive optimization techniques were recommended for each key performance factor. From the result obtained, the operational performance shows an average energy generation of approximately 389.71 GWh. The equipment availability factor averaged 54.08%, indicating moderate reliability. The energy availability factor was notably lower, averaging 11.99%, suggesting significant room for improvement. The capacity factor averaged 9.39%, while the plant use factor was relatively high at 80.59%, demonstrating efficient operational usage. The load factor was low, with an average of 0.10, pointing to potential underutilization of capacity. The shortfall in performance levels is attributed to less plant availability due to overdue overhauling of some units resulting in frequent breakdowns/failures, obsolete technology, aging plant equipment, instability of the national grid system, and disruption in gas supply among others. The recommended strategic techniques are designed to address the specific challenges associated with each factor, thereby enhancing the overall operational performance of the power plant and other power generation companies for maximum productivity.

Adaptive Spectrum Sensing in Cognitive Radio Employing Multitaper Method and Higher-Order STBC Techniques

Tue, 19 Nov 2024 00:00:00 +0800

The effects of the non-parametric multitaper method (MTM) and symmetrical higher-order space-time block-code (STBC) with full diversity techniques on the performance of spectrum sensing (SS) in cognitive radio (CR) systems are studied. The overall wireless link performance can be improved by using the STBC techniques to combat the multipath fading effects in wireless channels. The MTM is integrated with the STBC for spectrum estimation (SE), which will be termed multitaper spectrum estimation (MTSE)-STBC, and the given analysis of which is developed using the quadrature form approximation. Also, classical SE algorithms commonly focus on a fixed performance assessment method based on predefined false alarms or detection probabilities. Licensed users need to be protected against interference that opportunistic users might cause. An appropriate thresholding policy that varies concerning the designated values of false alarm and detection rates can achieve such protection. Utilization factors are instrumental in providing further protection for licensed users or occupied spectrum holes, and they need to be determined in advance and adopted in the detection policy. If utilization factors are not desirable, then a fine selection of appropriate threshold values will need to be decided as they have a major impact on the overall error probability performance. Using analytical and simulation methods, the assessment results revealed improved performance of the MTSE-STBC compared to other classical methods such as the Periodogram especially in the aspects of efficient transmission and less error probability. The proposed adaptive thresholding technique also proved useful in coping with different SE settings.

Reformulated Mathematical Models for Correlating the Solubility of Solid Drugs in Supercritical Carbon Dioxide

Vikram Ramalingam, Amrithaa Raghavan , Chandrasekhar Garlapati — Mon, 25 Nov 2024 00:00:00 +0800

Solubility is the fundamental thermodynamic key parameter influencing supercritical technology in practice. In the literature, several solubility models are available to represent the solubility data. They are classified into several categories based on their origin and the background on which they were developed. Some important categories of solubility models are solvate complex models, mathematical models, and phase equilibrium models. Among them, mathematical models have shown good correlation efficiency for several solute-solvent systems. However, some do not comply with the fundamental phase rule in their functional form, which can cause them to be termed redundant models. Therefore, the current investigation aims to address the redundant nature of some mathematical models. Models considered in the work relate solubility as a function of temperature, pressure, and density in a nonlinear relationship (i.e., y₂ = f (T, P, ρ₁)). Reformulation was aimed at converting solubility models to a dimensionally consistent form in which the mole fraction of the solute is represented as a function of the reduced density of the solvent and the reduced temperature. Thus, all solubility models considered in the work are converted to y₂ = f(T_r, ρ_r). Further, existing models and reformulation models were tested with four standard solute-solvent systems namely, naphthalene-SCCO₂, anthracene-SCCO₂, phenanthrene-SCCO₂, and salicylic acid-SCCO₂. Finally, the comparison between the existing and the reformulated models was done in terms of global values of AARD%, R², R²_adj, and ΔΑΙC. From the results, it is quite clear that reformulated models are showing better results than the existing models.

A Comprehensive Review on Corrosion Detection Methods for Aircraft: Moving from Offline Methodologies to Real-Time Monitoring Combined with Digital Twin Technology

Indu Elizabeth, Harish C. Barshilia — Mon, 25 Nov 2024 00:00:00 +0800

Corrosion is a universal phenomenon which affects almost all the metals, resulting in their degradation. Timely and accurate detection and monitoring of corrosion is essential for maintaining the structural integrity and preventing catastrophic failure of the structures. In the aircraft industry, the selection of specific metallic materials is mainly based on the properties of the metals like weight, heat resistance, stiffness, strength, electrical properties, etc., rather than their resistance to corrosion. The cost of corrosion in aircraft is huge not only from the economic point of view, but also in terms of human life and safety. Detecting corrosion at the right time is of utmost importance in the battle against corrosion. There are many non-intrusive and indirect testing methods to identify corrosion in the aircraft structure. Herein, we discuss various techniques currently being used for detection of corrosion in aircraft, including the conventional and non-conventional non-destructive inspection (NDI) methods. These NDI techniques are based on visual and other field detection approaches but are less effective in inspecting corrosion in hidden areas. A new trend in this regard is the development of real-time corrosion sensors that support non-destructive testing and signal the onset of the corrosion in aircraft structures. Artificial Intelligence (AI) techniques are gaining attention in corrosion detection nowadays. This review discusses various types of offline NDI methods, real-time corrosion sensors and AI techniques with their associated challenges in corrosion detection. We have also deliberated on the possibility of integration of these methods to develop a digital twin of the physical condition of the aircraft to improve accuracy and effectiveness of corrosion detection.