Engineering Journal (Faculty of Engineering, Chulalongkorn University, Bangkok)
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Influence of Cold Atmospheric Plasma Treatment on Fresh-Cut Mango Shelf-Life Extension
Get PDFEmergence of cold plasma technology has demonstrated a great potential for decontaminating fresh fruits and vegetables due to its non-thermal characteristics. A packed-bed plasma reactor with maximum high voltage of 8 kVDC was developed to generate gaseous reactive radicals from ambient air for post-process decontaminating of the fresh-cut and ready-to-eat mango. This Cold Atmospheric Plasma (CAP) machine employed gas from the glow discharge and a mixture of this gas with fine mist fog to inactivate microbial load. Average Total Plate Count (TPC) of untreated or controlled fresh-cut mango was observed to be above a maximum TPC requirement of 6 log CFU/g on the 5th day of 4°C refrigerated storage, while those of the CAP treatment reached the maximum TPC requirement on the 10th day. The CAP treatment of fresh-cut mango samples without- and with-fog presented significant microbial reductions of 2.09 and 1.87 log CFU/g, respectively, more than controlled samples on the 10th day of storage. Moreover, a browning-process deceleration of treated mango samples with CAP could be observed from L*a*b* without affecting samples’ pH and acidity during 10-day storage. Therefore, the CAP treatment revealed a strong possibility to extend a shelf-life of fresh-cut mango in the refrigerated storage
Monitoring Damage in PC Slabs by Modal and Ultrasonic Tests
Get PDFThe effectiveness of modal and ultrasonic tests for monitoring the damage in precast prestressed concrete slabs was experimentally investigated. Four slabs with two different span lengths and corresponding modes of failure (interfacial shear and flexural failures) were subjected to loading steps until failure. The variations in fundamental natural frequency, damping ratio, ultrasonic pulse velocity (UPV), and ultrasonic wave attenuation in relation to the damage severity and failure mode were investigated and compared. It was observed that the natural frequency was sensitive to flexural crack development. A significant change in the damping ratio was obtained in the slabs with moderate damage. The UPV was not affected by a moderate degree of interfacial shear damage and a low degree of flexural damage; however, it was strongly related to the progression of flexural damage at the severe stage. Among the various indexes, ultrasonic wave attenuation was most sensitive to the development of damage. The method could detect interfacial-shear and flexural cracks at an early stage
Design of Control Systems with Multiple Backlash Nonlinearities Subject to Inputs Restricted in Magnitude and Slope
Get PDFThis paper develops a computational method for designing a control system that is an interconnection of transfer functions and multiple decoupled backlash nonlinearities where each backlash is modelled as an uncertain band containing multi-valued functions. The design objective is to ensure that the system outputs and the nonlinearity inputs always stay within their prescribed bounds in the presence of all inputs whose magnitude and whose slope are bounded by respective numbers. By using a known technique, each backlash is decomposed as a linear gain and a bounded disturbance. Essentially, the original design problem is replaced by a surrogate design problem that is related to a linear system and thereby can readily be solved by tools available in previous work. Moreover, as a result of using the convolution algebra A, the method developed here is applicable to rational and nonrational transfer functions. To illustrate the usefulness of the method, linear decentralized controllers are designed for a binary distillation column where valve stiction characteristics are taken into account
Effect of Thickness Eccentricity on the Stress Intensity Factors for a Pipe with a Single Internal Radial Crack under Internal Pressure
Get PDFThe thickness eccentricity of a pipe occurs due to manufacturing limitations and may be exacerbated by service-induced degradation mechanisms. Fracture and remaining life assessments of a cracked eccentric pipe require a solution for the crack-tip parameters, e.g., the stress intensity factors (SIFs). However, the SIFs for this problem have not been examined. This study aimed to develop SIFs for an eccentric pipe with an infinitely longitudinal crack nucleated from an inner wall at the thinnest location of the pipe cross-section subjected to internal pressure. The problem was simplified to a cracked eccentric ring in a plane-strain condition, and finite element analysis was utilized for the determination of the SIFs, which were presented in tabulated form and empirical relation. The SIFs included a wide range of configuration parameters, i.e., a thin to thick-walled pipe, a shallow to deep crack, and a concentric pipe to a pipe with moderate thickness eccentricity. The need to consider the effect of eccentricity in SIFs calculation increased when the relative thickness of a pipe decreased and the relative crack depth increased
A Theoretical Approach to Optimize the Pipeline Data Communication in Oil and Gas Remote Locations Using Sky X Technology
Get PDFOil, gas, and water distribution networks in remote locations require optimized data transmission from their sources to prevent or detect leakage or improve production flow in their manufacturing units. Remote oil and gas installations frequently encounter substantial obstacles in terms of data connectivity and transfer. Slow data transmission rates, data loss, and decision-making delays can all be caused by a lack of dependable network infrastructure, restricted bandwidth, and severe climatic conditions. The purpose of this research work is to identify critical concerns concerning data communication and data transfer in oil and gas distant areas and to investigate feasible approaches to these challenges. The survey was carried out to gather feedback from oil and gas experts on issues concerning data transmission in remote locations. This study provides a theoretical approach to optimizing data transmission and communication in remote areas using Sky X technology. This study presents a new theoretical method that improves the performance of IP over satellite using the critical aspects of data transmission issues from experts. This technology's contribution can improve the reliability of all users on a satellite network by delivering all features with a successful data transfer rate discreetly. This attempt may also aid oil and gas companies in optimizing data transmission/communication in remote regions
Design and Evaluation of mmWave MIMO Networks Using 28 and 60 GHz in Urban Areas
Get PDFA new alternative network is critical since cellular users are increasing year after year and network capacity is becoming insufficient. Currently, network design prioritizes not only performance but also energy efficiency. The MIMO system has existed for a long time and has been shown to improve system performance. A mmWave technology is currently one of the 5G enabling technologies that use high frequencies to enable speeds of up to 1 Gbps while maintaining high capacity and low latency. Thus, the combination of mmWave technology and MIMO systems is one of the challenges for implementing 5G technology with high performance and energy efficiency in urban areas. This paper, therefore, designs and evaluates the mmWave MIMO network using 28 and 60 GHz frequencies in urban areas, especially in Banda Aceh city. Then, this paper analyzes the designed network performance by considering coverage area, SINR, throughput, and energy efficiency. The designed mmWave MIMO system uses different antennas: 4, 8, 16, and 32. Simulation results indicate that the mmWave MIMO 28 GHz network has a larger coverage area, higher SINR, and more energy efficiency than the mmWave MIMO 60 GHz network. The highest energy efficiency is achieved in the network using a 16-antenna. On the other hand, the throughput of a mmWave MIMO 28 GHz network is lower than that of a mmWave MIMO 60 GHz network. The mmWave MIMO 28 GHz network has demonstrated advantages that make it ideal for use in urban areas, particularly in Banda Aceh
A Multi-Channel Noise Estimator Based on Improved Minima Controlled Recursive Averaging for Speech Enhancement
Get PDFThis article introduces an extension of the improved minima-controlled recursive averaging noise estimation from single to multi-channel speech enhancement systems. With the spatial information of microphone array signals being fully exploited, more accurate estimate of the noise spectrum can be obtained over the single-channel counterpart. Computer simulation demonstrates superior performance of the proposed noise estimator in terms of noise tracking performance and noise estimation error. Furthermore, the use of the proposed technique with the multi-channel Wiener filter yields improved signal-to-noise ratio and speech distortion
Creep Life Prediction for Hastelloy XR Using the Omega Method
Get PDFThis paper applies the Omega method to creep life prediction for Hastelloy XR at temperatures ranging from 850 to 950oC in an air environment. The creep data were obtained from literature. Three life prediction scenarios were studied including constant stress, constant load, and continuous monitoring where creep data is simulated for sequential acquisition. The constant stress creep data at each temperature were used to determine the Omega model parameters, and empirical equations for each parameter were developed. The predicted creep lives under constant stress were within a factor of 2 in almost all cases. For a life prediction under constant load, the actual applied stress was estimated and used in the creep constitutive equation as well as for calculating the model parameters. The predicted creep lives were also to be within a factor of 2 in almost all cases. The Omega model was found to be applicable to a continuous creep data acquisition scenario as well. An appropriate scheme for continuous monitoring scenario was suggested, and statistical analysis by the Monte Carlo simulation was demonstrated
Predicting the Product Classification of Hot Rolled Steel Sheets Using Machine Learning Algorithms
Get PDFThe mechanical properties of the SAPH440 hot rolled steel sheet are mainly controlled to satisfy product specifications. Three mechanical properties including the yield strength, ultimate tensile strength, and elongation are measured and utilized in product classification. Based on these properties, the steel is classified into 3 grades: Class 1 (meets specification), Class 2 (moderate quality), and Class 3 (low). However, various factors can affect the mechanical properties, leading to a long setup time for initial production runs. Therefore, this paper aims to improve the accuracy of these predictions by using machine learning algorithms. The results of experiments showed that the random forest algorithm had the best performance, with an accuracy of 70.0% and a macro average F-1 score of 70.0%. This more accurate prediction can reduce the initial setup time and save 37,000 USD per grade in trial run costs
Potato Leaves Blight Disease Recognition and Categorization Using Deep Learning
Get PDFPotato cultivation is vital in numerous countries, contributing to food security and economic value. However, crop diseases, particularly early and late blight, pose significant challenges to potato production. The accurate diagnosis of these diseases remains unclear to many individuals. This study leverages the increasing penetration of smartphones and recent advancements in deep learning to develop a Convolutional Neural Network (CNN) model for real-time detection of early and late blight in potatoes. The dataset was pre-processed by normalizing, dividing, and extracting images using the Python data processing library. The approach incorporates slight variations in the network layers to optimize the model's performance. The method was evaluated using classification optimizers, metrics, and loss functions and further refined using layer-by-layer TensorBoard analysis. Hyperparameters such as features, labels, validation split, batch size, and training epochs were carefully selected. The final model demonstrated promising results, achieving an accuracy of 96.09% on the survey dataset. Experimental findings highlight the approach's potential for automatically detecting both early, late blight and healthy, thereby significantly improving the accuracy of disease diagnosis