Development of Feed-Forward Back-Propagation Neural Model to Predict the Energy and Exergy Analysis of Solar Air Heater

In the present work, Artificial Neural Network (ANN) model has been developed to predict the energy and exergy efficiency of a roughened solar air heater (SAH).  Total fifty data sets of samples, obtained by conducting experiments on SAHs with three different specification of wire-rib roughness on the absorber plates, have been used in this work. These experimental data and calculated values of thermal efficiency and exergy efficiency have been used to develop an ANN model. Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) learning algorithm were used to train the proposed ANN model. Six numbers of neurons were found with LM learning algorithm in the hidden layer as the optimal value on the basis of statistical error analysis. In the input layer, the time of experiments, mass flow rate, ambient temperature, mean temperature of air, absorber plate temperature and solar radiation intensity have been taken as input parameters; and energy efficiency and exergy efficiency have been taken as output parameters in the output layer. The 6-6-2 neural model has been obtained as the optimal model for prediction. Performance predictions using ANN were compared with the experimental data and a close agreement was observed. Statistical error analysis was used to evaluate the results.Citation: Ghritlahre, H. K. (2018). Development of feed-forward back-propagation neural model to predict the energy and exergy analysis of solar air heater. Trends in Renewable Energy, 4, 213-235. DOI: 10.17737/tre.2018.4.2.007

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Solar thermal systems (STS) are efficient and environmentally safe devices to meet the rapid increasing energy demand now a days. But it is very important to optimize their performance under required operating condition for efficient usage. Hence intelligent system-based techniques like artificial neural network (ANN) play an important role for system performance prediction in accurate and speedy way. In present paper, it is attempted to scrutinize the approach of ANN as an intelligent system-based method to accurately optimize the performance prediction of different solar thermal systems. Here, 25 research works related to various solar thermal systems have been reviewed and summarized to understand the impact of different ANN models and learning algorithms on performance prediction of STS. Using ANN, a brief stepwise summary of researchers’ work on various STS like solar air heaters, solar stills, solar cookers, solar dryers and solar hybrid systems, their predictions (results) and architectures (network and learning algorithms) in the literature till now, are also discussed here. This paper will genuinely help future researchers overview the work concisely related to solar thermal system performance prediction using various types of ANN models and learning algorithm and compare it with other global methods of machine learning. Citation: Ahmad, A., Ghritlahre, H. K., and Chandrakar, P. (2020). Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review. Trends in Renewable Energy, 6, 12-36. DOI: 10.17737/tre.2020.6.1.0011

Intra articular distal radius fractures and volar plate fixation: a prospective study

Background: Despite being one of the most common fractures encountered in patients, intra-articular distal radius fractures still pose therapeutic challenge to Orthopaedic surgeons. With the advent of locking plates, the fixation of these fractures has been made better, specifically by fixed angle volar locking plate. This study investigates the efficacy of these plates using volar approach, functional and clinical outcome, in addition to the radiological alignment.Methods: Thirty patients with closed distal radius fractures, with AO TYPE B3, B4, AND C fracture pattern, operated with distal radius plate fixation using volar approach, were included in the study during the period of August 2014 to August 2016. With a minimum follow up of six months, radiological outcome was analysed and functional outcome recorded (Gartland and Werley’s demerit scoring system).Results: With a mean age of 42 years and follow up of six months, the range of movement of the wrist was very satisfactory, and the mean grip strength was 80% of the opposite wrist. Radiological parameters were well‑maintained, and functional parameters by Gartland and Werley showed a significant improvement in most of the patients during the follow‑up period. The complication rate was less and insignificant.Conclusions: Primary volar plate fixation of intraarticular distal radius fracture provides a stable construct that helps in early mobilization, thereby better functional outcomes and minimizes chances of delayed/malunion

Estimation of yield and grain qualities of marker assisted backcross derived lines of submergence rice against sheath blight disease

Sheath blight caused by Rhizoctonia solani is one of the most devastating diseases of rice (Oryza sativa) and causes enormous yield losses over the world after blast, the disease can cause yield loss upto 50 per cent in advanced stage and adversely affects quality of straw. Breeding for resistant varieties is the only viable option to combat the disease efficiently. In this study, our findings showed a significant increase in number of spikelet’s per panicle (3.45 %), test weight (0.62 %) and grain yield (0.72 %) compared to recurrent parent Swarna sub-1. The range of mean performance of 18 BC2F1 selected improved lines varied for per cent disease severity from 26.75 to 43.58 at 16 days after inoculation. Among the 18 improved lines, only four lines (Swarna sub-1-6, Swarna sub-1-32, Swarna sub-1-13 and Swarna sub-1-29) showed resistance score of 1-3. The remaining fourteen lines showed moderate resistance with a score of 3-5. Hence, the resistance line could be exploited in sheath blight resistance breeding programme and the same line can also be released as a variety against sheath blight of rice after testing over multilocation trails

Analysis to energy consumption characteristics and influencing factors of terminal building based on airport operating data

Airport terminals have much higher average energy consumptions than normal public buildings. This is caused by their complicated space features and operation characteristics. In China, large modern airports were built from around 2004, which means our understanding to the real energy performance of these buildings lag behind the design practice and with feedback from operation experience, both design or operation of modern airports shall be improved in terms of energy efficiency. Nanning Airport provided an excellent example for that purpose, with comprehensive operation data collected during 2016–2019. The data were firstly presented in this paper, followed by clustering and correlation analysis to establish the correlation among potential factors. From the analysis, the major factors influencing energy consumption were identified as passenger flow, meteorological parameters and supply fan frequency. Regression analysis was performed based on monthly data and equations were provided for the calculation of the terminal total energy consumption and its three sub components according to the input parameters of passenger flow data and meteorological data. These equations provide guidance on energy supply for the operation of this airport and more importantly can be used as valuable reference when designing airports in similar conditions

ANN prediction of corrosion behaviour of uncoated and biopolymers coated cp-Titanium substrates

The present study focuses on biopolymer surface modification of cp-Titanium with Chitosan, Gelatin, and Sodium Alginate. The biopolymers were spin coated onto a cp-Titanium substrate and further subjected to Electrochemical Impedance Spectroscopic (EIS) characterization. Artificial Neural Network (ANN) was developed to predict the Open Circuit Potential (OCP) values and Nyquist plot for bare and biopolymer coated cp-Titanium substrate. The experimental data obtained was utilized for ANN training. Two input parameters, i.e., substrate condition (coated or uncoated) and time period were considered to predict the OCP values. Backpropagation Levenberg-Marquardt training algorithm was utilized in order to train ANN and to fit the model. For Nyquist plot, the network was trained to predict the imaginary impedance based on real impedance as a function of immersion periods using the Back Propagation Bayesian algorithm. The biopolymer coated cp-Titanium substrate shows the enhanced corrosion resistance compared to uncoated substrates. The ANN model exhibits excellent comparison with the experimental results in both the cases indicating that the developed model is very accurate and efficiently predicts the OCP values and Nyquist plot

An experimental study of solar air heater using arc shaped wire rib roughness based on energy and exergy analysis

In the present study, energy and exergy analysis has been evaluated for roughened solar air heater (SAH) using arc shaped wire ribs. To achieve this aim, two different types of flow arrangement have been considered. These arrangements are: apex upstream flow and apex downstream flo. In addition to this, a smooth duct SAH has been used for comparative study. The experiments were performed using the mass flow rate of 0.007– 0.022 kg/s on outdoor condition at Jamshedpur city of India. The absorber plate roughness geometry has been designed with relative roughness height 0.0395, rib size 2.5 mm, relative roughness pitch 10 and arc angle 60◦ . The energetic and exergetic performances have been examined on the basis of the first and second law of thermodynamics. According to the results, there is observed to be the maximum thermal efficiency and exergy efficiency as 73.2% and 2.64%, respectively, for apex upstream flow SAH at 0.022 kg/s, while, at same mass flow rate the maximum thermal efficiency and exergy efficiency is obtained as 69.4% and 1.89%, respectively, for apex downstream flow SAH. In addition to this, results reported that the maximum outlet temperature and temperature difference observed at lower mass flow rate. Also examined the outlet air temperature of SAH with various mass flow rates is very important for both analysis

G × E interaction and adaptability of rice cultivars in SRI and normal production systems

In any breeding program, it is necessary to screen and identify phenotypically stable genotypes that could perform uniformly under different environmental conditions. Such a breeding effort requires basic information of genotype × environment (G × E) interaction. Twenty genotypes including hybrids and aromatic rice were evaluated in 8 environments in two production systems viz; System of Rice Intensification (SRI) and normal cultivation environments during kharif season (May–October) 2009. The experiment was laid down in RBD with two replications in a plot of 1 m2. Pooled analysis for G × E interaction and stability revealed that the genotypes and environments were highly significant (p < 0.01) for all twelve characters studied. The G × E interaction was significant for six traits including all key components of SRI except tillers no. Both linear and non-linear components contributed towards G × E interaction. Stability parameters identified genotypes PR-114 and HKR-47 as stable for grain yield per plant and HKR-127, HKR-120, CSR-30, Pusa-1121 and IR-64 for test grain weight. Genotypes identified suitable for favourable environments were HKR-126, HSD-1, PAU-201and Govind while for unfavourable environment were HSD-1, HKRH-1094, HKR-48 and PAU-201 for different traits. IR 64 and Pusa 1121 registered 24.31 and 12.54% increase in yield in SRI over normal production system. These genotypes need to be tested in macro environments over space and time and could be utilized for direct cultivation as well as for improvements of other cultivars