JCT College Of Engineering And Technology

In solar thermal applications, the performance of a solar absorber panel with a reflector is critical. In sense, different absorbers are used to enhance thermal efficiency. In addition to this, various coatings on solar absorber panels are introduced, and the performance of the same coated panels are examined with experimental study by design of experiment concept. For the reason, this work utilized the black-chrome coating (BC) and nickel-cobalt-coating (Ni-Co) on copper solar absorber panel. The reflectors were also used to maximize the incident solar radiation on the absorber. In this experiment the process variables are flow rate, collector angle, and reflector angle and the thermal efficiency of solar panel is the response. The response surface methodology with Box Behnken design is used to collect the experimental data from experimental setup. The collected data from designed experiment are analyzed by using Analysis of Variance Table (ANOVA). From this the coating on the absorber panel and reflectors are improved the thermal efficiency up to 89.3% of the Black – Chrome solar flat plate collector than Ni-Co panel.

Friction stir welding (FSW) was employed to join 10 mm thick AA2024-T6 aluminium alloy plates to overcome the fusion welding related problems such as grain coarsening in fusion zone, heat affected zone (HAZ) softening, lower joint efficiency and premature joint failure. The center cracked test (CCT) specimens were used to evaluate the fatigue crack growth rate of the welded joints. The microstructural features of joint were characterized using optical microscopy (OM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The fractured surfaces of CCT specimens were analyzed using scanning electron microscopy. Results showed that the FSW joints exhibit lower fatigue strength than the parent metal due to the dissolution of second phase strengthening precipitates. The post weld heat treatment (PWHT) specifically solution treatment (ST), artificial ageing (AA), solution treatment and ageing (STA) were employed to improve the fatigue properties of FSW joints. It was observed that the STA treated FSW joint exhibit higher fatigue life than the ST and AA treated joints. It is attributed to the precipitation of second phase strengthening precipitates in welded joint after STA.

Abstract The best aluminum alloys for construction are those that incorporate copper. However, welding engineers find it difficult to join aluminium and its alloys as a result of cracking. One of the popular methods for joining nonferrous materials, especially aluminum alloys, is friction stir welding (FSW). A tensile strength of 75 % to 85 % of the basic material strength is produced by FSW joints. The majority of studies have documented a reduction in strength as a result of incomplete melting, creating a soft region at the boundary between the thermo – mechanically influenced zone and the stir zone. The current effort has focused on using the shot peening method to reduce the softness at the interface. According to the test findings, the nickel shot-peened joint produced a stronger joint than the traditional FSW joint. The shot-peened joint has gained 7 % additional strength compared to untreated joint.

In this research, the friction and wear of AA7075 nanocomposites reinforced with graphene and graphite were studied. Graphene’s inclusion dramatically enhanced the material’s mechanical characteristics, friction, and wear resistance. AA7075 is strengthened with less graphene, and AA7075, reinforced with more graphite, exhibits similar wear and friction behavior. Wear rate and coefficient of friction predictions for AA7075-graphene nanocomposites were made using five machine learning (ML) regression models. ML simulations reveal that the wear and friction of AA7075-graphene composites are most sensitive to the proportion of graphene presence, the loadings, and the hardness.

Cloud storage provides a potential solution replacing physical disk drives in terms of prominent outsourcing services. A threaten from an untrusted server affects the security and integrity of the data. However, the major problem between the data integrity and cost of communication and computation is directly proportional to each other. It is hence necessary to develop a model that provides the trade-off between the data integrity and cost metrics in cloud environment. In this paper, we develop an integrity verification mechanism that enables the utilisation of cryptographic solution with algebraic signature. The model utilises elliptic curve digital signature algorithm (ECDSA) to verify the data outsources. The study further resists the malicious attacks including forgery attacks, replacing attacks and replay attacks. The symmetric encryption guarantees the privacy of the data. The simulation is conducted to test the efficacy of the algorithm in maintaining the data integrity with reduced cost. The performance of the entire model is tested against the existing methods in terms of their communication cost, computation cost, and overhead cost. The results of simulation show that the proposed method obtains reduced computational of 0.25% and communication cost of 0.21% than other public auditing schemes.

The global standards in the field of industrial automation are maintained in industries by completely digitizing their manufacturing process with industry 4.0 standard. Internet of Things (IoT) enables the conservation of cultural heritage with proper assistance on data management on the data collected from the sensors. However, energy efficient conservation is required to monitor the IoT sensors in order to deal with building a better infrastructure. In this paper, we develop a bio-inspired algorithm which can automate the entire furnace monitoring and controlling system in order to eliminate the human intervention involved in the physical process. The algorithm is blended as a web-based remote application for the better control of the tasks involved, energy utilized, and its subsequent log-report maintenance. The entire system employs Wi-Fi communication for data transfer from device to cloud where the stored data including temperature log, forth coming schedule, and process graphic are maintained by the proposed algorithm to predict the machine failure at an earlier stage. The real-time prototype system is supported by a heat treatment process that is completely automated using IoT to monitor and maintain the temperature during the production of metal casting process.

Assignment problem (AP) is a well known topic and is used very often in solving problems of engineering and management science. In this problem a ij denotes the cost for assigning the j th job to the i th person. The cost is usually deterministic in nature. In this paper ij has been considered to be trapezoidal and triangular numbers denoted by ij which are more realistic and general in nature. Robust's ranking method [10] has been used for ranking the fuzzy numbers. The fuzzy assignment problem has been transformed into crisp assignment problem in the linear programming problem form and solved by using Hungarian method; Numerical examples show that the fuzzy ranking method offers an effective tool for handling the fuzzy assignment problem.

Abstract This study evaluates the thermal and mechanical properties of epoxy hybrid composites reinforced with date palm fiber (DPF) and kenaf fiber (KF) at various fiber loadings. Flexural properties, thermogravimetric analysis (TGA), and dynamic mechanical analysis (DMA) of hybrid composites were reported. The flexural properties revealed the effect of fiber ratio on flexural strength and modulus; a higher kenaf ratio showed a higher flexural modulus. TGA revealed the effect of lignin present in the DPF, which affected the glass transition significantly. In DMA characterization, storage modulus revealed that 7DP3K showed higher modulus in the initial stage and had better glass transition than others. The loss modulus showed that the peak of 7DP3K had higher loss modulus, glass transition at a higher temperature, and wider relaxation period. Damping factor also showed that 7DP3K had peak at a higher temperature. The result showed that KF has positively impacted on DPF to improve the mechanical and thermal properties. The hybrid fibers in epoxy composites were effective in increasing the dynamic mechanical properties and flexural strength ascribed to the improved matrix/fiber bonding. The possible applications of the hybrid composites may be in building industry and automobiles. This study gives directions for further research on comparison of treated hybrid composites with untreated ones.

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

Globally skin cancer is one of the main cause of death in humans. Early diagnosis plays a major role in increasing the prevention of death rate caused due to any kind of cancer. Conventional diagnosis of skin cancer is a tedious and time-consuming process. To overcome this an automated skin lesion classification must develop. Automated skin lesion classification is a challenging task due to the fine-grained variability in the visibility of skin lesions. In this work dermoscopic images are obtained from the International Skin Image Collaboration Archive 2016 (ISIC 2016). In the proposed method the analysis and classification of skin lesions is done with the help of a Convolution Neural Network (CNN) along with the hand crafted features of dermoscopic image using Scattered Wavelet Transform as additional input to the fully connected layer of CNN, which leads to an improvement in the accuracy for identifying Melanoma and different skin lesion classification when compared to the other state of the art methods. When raw dermoscopic image is given as an input to the CNN and feature values of segmented dermoscopic image as input to the fully connected layer as an additional information, the proposed method gives a classification accuracy of 98.13% for identification of Melanoma and the accuracy achieved for classification of skin lesions is 93.14% for Melanoma Vs Nevus, 95.4% for Seborrheic Keratosis (SK) vs Squamous Cell Carcinoma (SCC), 96.87% for Melanoma vs Seborrheic Keratosis (SK), 95.65% for Melanoma vs Basal Cell Carcinoma (BCC), 98.5% for Nevus vs Basal Cell Carcinoma(BCC).

Autonomous vehicles want reliable and strong sensor suites and alert systems. This paper discusses the composition and performance of a sophisticated monitoring and alert system for automobile vehicle parameters. The number of automobiles has also grown quickly to meet the enormous population. Additionally, this resulted in an increase in accidents. The accident prevention strategies now in use are all static and dated. Additionally, there is no reliable accident detection system. Automobile vehicle parameters are continuously monitored by a micro-controller which stores the data logs containing vehicle parameter data into a sheltered digital memory card and in the cloud storage. The system doesn't solely record the vehicle parameters data of the automobile periodically, but also actively monitors for any sudden vehicle accident detection. The sensor may facilitate folks to analyze the accident quickly and lawfully when a collision happens to alert the emergency services to that location. The system will update the information whenever an abnormal system event happens. A black box in a vehicle gather driving information about the vehicle before, during and after a crash. The data gathered includes, speed, acceleration, braking, steering and air-bag deployment. The Automotive black box system can aid with vehicle safety, increase collision victim care, assist insurance corporations with vehicle crash investigations, and improve road conditions to reduce death rates. According to experimental findings, the proposed technique achieves 29.3103%, 22.70 %, 18.103% and 11.206 % higher accuracy compared to RFID, SVM,CNN and RNN Methods.

This research is a comparative study of untreated and treated date palm fiber (DPF) and kenaf (KF)-reinforced epoxy hybrid composite and explores the properties of DPF. The prepared hybrid composites were characterized in terms of tensile properties, impact properties, physical behavior, water absorption, and thickness swelling. The composites were prepared by hand layup technique. Treated 30% date palm and 70% kenaf (T-3DP7K) showed the highest tensile strength (26.45 MPa) and modulus (4.54 GPa) among untreated and treated hybrid composites. The scanning electron microscope revealed the behavior of fiber and polymers and also the cause of tensile test failure. The impact strength of T-1DP1K revealed highest impact resistant (5493 J/m) among untreated and treated hybrid composites. Remarkably, alkali-treated hybrid composites reduced the water absorption and thickness swelling compared to the untreated composites. In untreated composites, higher DPF content composites showed higher moisture content and dimensional instability. Overall, we concluded that among all hybrid composites, T-3DP7K showed better tensile properties, lowest water absorption, and lowest thickness swelling, and T-7DP3K showed better impact properties.

This study has been carried out to investigate the production of biogas by anaerobic digestion of solid-phase kitchen food waste using an artificial neural network. The network was used to model and optimise biogas production using mixed substrates of food waste with cow dung. The substrate mix percentage, plant pH level, digestion period and digester temperature were used as input parameters for the model, with biogas yield as the output. Food waste and cow dung were mixed at different compositions to a total mass of 2 kg and placed in 21 miniature digesters. The input and output parameters from the digesters were then considered in the model. The highest biogas performance level of 375 ml/g volatile solids on the 25th day of digestion was achieved by a substrate profile of 80% food waste and 20% cow dung at a temperature range of 30–40°C. On the basis of these results, kitchen food waste is shown to be highly biodegradable and an effective source of biogas.

Contamination HM is an important issue associated with the environment, and it requires suitable steps for the reduction of HMs in water at an acceptable ratio. With modern technologies, this could be possible by enabling the carbon adsorbents to adsorb the pollutions via deep learning strategies. In this paper, we develop a model on detection and prediction of presence of HMs from drinking water by analysing the adsorbents from residuals using deep learning. The study uses dense neural networks or DenseNets to analyse the microscopic images of the residual adsorbents. The study initially preprocesses and extracts features using standardised procedure. The DenseNets are used finally for detection purpose, and it is trained and tested with standard set of microscopic images. The experimental results are conducted to test the efficacy of the deep learning model on detecting the HM composition. The results of simulation show that the proposed deep learning model achieves 95% higher rate of detecting the HM composition from the adsorption residuals than other methods.

In the present work, an experimental investigation is being carried out to increase the quality of fresh water produced by a tubular solar still connected to an electrical heater that is connected to a photovoltaic (PV) panel. With an effective evaporative area of 0.1425 m2, experiments were conducted and analysed for the climatic condition of Chennai, India. On the basis of the amount of fresh water generated by a tubular solar still without the use of an external heater, a comparative study is conducted. With the addition of electrical heater with the absorber, the average temperature of water is improved from 49 °C to 52 °C. The cumulative yield from TSS and TSS with external heater is found as 2.5 and 3.4 kg with a daily efficiency of 30.28 and 59.52% respectively. Results also revealed that the accumulated fresh water produced from the TSS with external heater is improved by 32.67% than TSS without additional heater.

Various chemical treatments such as mercerization (alkali treatment), permanganate treatment, and benzoyl chloride treatment were performed on palmyra palm leaf stalk fibers. Composites were fabricated by hand layup method followed by compression molding process using unsaturated polyester as matrix. Two types of composites were fabricated, one by using treated continuous palmyra palm leaf stalk fibers and other by hybridizing nonwoven glass fiber mat and alkali treated continuous palmyra palm leaf stalk fibers with bilayer arrangement. In both the cases palmyra palm leaf stalk fiber is laid unidirectionally for composite fabrication. The composites were tested for its tensile, flexural, impact, and dynamic mechanical properties. There was an increase of 33% in tensile, 55% in flexural, and 50% in impact strength for benzoyl, permanganate, and alkali treated composites, respectively, compared to untreated fiber composites. But all the treated fiber composites did not show much variation in properties. Hybridization resulted in 54, 36, and 58% improvement in tensile, flexural, and impact properties, respectively, in comparison to unhybridized treated unidirectional palmyra palm leaf stalk fiber composites. Reinforcement of alkali treated fibers and hybridization of glass fibers enhanced the loss and storage modulus of the composites, increased glass transition temperature (T g ), and also reduced the tan δ peak. A positive shift in tan δ was also evident for all the composites.

Nano-alumina-doped catechol formaldehyde polymeric composite was prepared, characterized, and applied as an adsorbent for the removal of an anionic dye Congo red (CR) and a cationic dye SafraninO (SF), by adsorption process especially from aqueous solutions. Characterizations such as particle size distribution, zeta potential, BET, FTIR, and FESEM-EDAX were carried out for the adsorbent prepared. All experiments were conducted at the batch condition to study the effects of initial dye concentration (CR: 30–90 mg/L and SF: 10–50 mg/L), pH (2–11), temperature (25–55°C), and adsorbent dosage (0.05–0.3 g) on dye removal. The isotherm models (Langmuir, Freundlich, and Temkin) were analyzed for this adsorption work. The kinetic data obtained were analyzed by the pseudo-first-order, pseudo-second-order, Bangham, and Chien–Clayton equations. Dyes adsorption data were well fitted with the Freundlich isotherm equilibrium model and the pseudo-second-order kinetic model. Study results suggested that the nano-alumina-polymeric composite could be an effective adsorbent for anionic dye rather than cationic dye.

A fascinating problem in the fields of nanoscience and nanobiotechnology has recently emerged, and to tackle this, the production of metal oxide nanoparticles using plant extracts offers numerous benefits over traditional physicochemical methods. In the present investigation, ZnO nanoparticles were fabricated from Bauhinia racemosa Lam. (BR) leaves extract with various transition metal (TM) dopants (Ni, Mn, and Co). Plant leaves extract containing metal nitrate solutions were utilized as a precursor to synthesize the pristine and TM-doped ZnO nanoparticles. Structural, functional, optical, and surface properties of the fabricated samples were studied by using physicochemical and photoelectrochemical measurements. The organic pollutants tetracycline (TC), ampicillin (AMP), and amoxicillin (AMX) were used in the photocatalytic degradation assessment of the fabricated samples. Through X-ray diffraction (XRD) and transmission electron microscopy (TEM) investigation, the fabricated nanoparticles wurtzite crystal structure was verified. Moreover, Fourier transform infrared (FT-IR) analysis verified the existence of functional groups in the fabricated nanoparticles. The migration of electrons from the deep donor level and zinc interstitial to the Zn-defect and O-defect is related to the emission peaks seen at 468, 480, 534, and 450 nm in photoluminescence (PL) spectra. Co-ZnO nanoparticles demonstrated potent and excellent photocatalytic degradation performance for TC (91.09%), AMP (87.97%), and AMX (92.42%) antibiotics within 210, 180, and 150 min of visible light irradiation. Co-ZnO nanoparticles also demonstrated strong antimicrobial performance against Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Aspergillus flavus, Aspergillus niger, and Bacillus subtilis. Further investigation of in vitro cytotoxic potential against the A549 cell line (IC50 = 24 ± 0.5 μg/mL) utilizing MTT assay and the free radical scavenging performance of Co-ZnO nanoparticles estimated by DPPH assay utilizing l-ascorbic acid as a reference was also performed. Anti-inflammatory potential is also reviewed by comparing it with the standard drug Diclofenac, and the maximum activity was obtained for Ni-ZnO nanoparticles (IC50 = 72.4 μg/mL).

The comprehensive study of smoke deposited nano sized MgO as a catalyst for biodiesel production was investigated. The transesterification reaction was studied under constant ultrasonic mixing for different parameters like catalyst quantity, methanol oil molar ratio, reaction temperature and reaction time. An excellent result of conversion was obtained at 1.5 wt% catalyst; 5:1 methanol oil molar ratio at 55°C, a conversion of 98.7% was achieved after 45 min. The conversion was three to five times higher than those are reported for laboratory MgO in literature. This was mainly due to the enhancement of surface area of the catalyst and the activity of ultrasonic waves. Catalyst is easily recovered and reused up to eight times with easy regeneration steps. © 2013 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)

The present investigation involves the production of environmental-friendly heterogeneous catalyst from waste Scylla tranquebarica crab shell and optimization of process parameter for biodiesel production from sunflower oil. A complete characterization of the catalyst including catalytic transesterification reaction was studied using gas chromatogram (GC), thermal gravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM) and 1H nuclear magnetic resonance (1H NMR). An optimum conversion of 94.2% was achieved at 95°C; methanol--oil molar ratio 12:1; 8 wt % catalyst in 75 min. It was found that the catalytic activity has the ability to compete with the other conventional heterogeneous catalysts. This study includes optimal conditions for the removal of the leached catalyst in biodiesel to enhance product purity. This reaction follows a first-order reaction kinetics. The rate constant and activation energy were determined. Fuel properties of biodiesel produced were determined and compared with ASTM standards.