Search results for: deep eutectic solvent

Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 251

Authors: Niklas Panten, Eberhard Abele

Abstract:

This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

Procedia PDF Downloads 188

Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

Abstract:

Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

Procedia PDF Downloads 91

Authors: Haozhe Xiang

Abstract:

With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

Procedia PDF Downloads 62

Authors: Gaokai Liu

Abstract:

Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets.

Keywords: deep learning, defect detection, image segmentation, nanomaterials

Procedia PDF Downloads 143

Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

Abstract:

In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

Procedia PDF Downloads 338

Authors: Kanika Kalra, Harmeet Kaur, Dinesh Goyal

Abstract:

Total phenolics were extracted from waste orange peels by solvent extraction and alkali hydrolysis method. The most efficient solvents for extracting phenolic compounds from waste biomass were methanol (60%) > dimethyl sulfoxide > ethanol (60%) > distilled water. The extraction yields were significantly impacted by solvents (ethanol, methanol, and dimethyl sulfoxide) due to varying polarity and concentrations. Extraction of phenolics using 60% methanol yielded the highest phenolics (in terms of gallic acid equivalent (GAE) per gram of biomass) in orange peels. Alkali hydrolyzed extract from orange peels contained 7.58±0.33 mg GAE g⁻¹. By using the solvent extraction technique, it was observed that 60% methanol is comparatively the best-suited solvent for extracting polyphenolic compounds and gave the maximum yield of 4.68 ± 0.47 mg GAE g⁻¹ in orange peel extracts. DPPH radical scavenging activity and reducing the power of orange peel extract were checked, where 60% methanolic extract showed the highest antioxidant activity, 85.50±0.009% for DPPH, and dimethyl sulfoxide (DMSO) extract gave the highest yield of 1.75±0.01% for reducing power ability of the orange peels extract. Characterization of the polyphenolic compounds was done by using Fourier transformation infrared (FTIR) spectroscopy. Solvent and alkali hydrolysed extracts were evaluated for antibacterial activity using the agar well diffusion method against Gram-positive Bacillus subtilis MTCC441 and Gram-negative Escherichia coli MTCC729. Methanolic extract at 300µl concentration showed an inhibition zone of around 16.33±0.47 mm against Bacillus subtilis, whereas, for Escherichia coli, it was comparatively less. Broth-based turbidimetric assay revealed the antibacterial effect of different volumes of orange peel extracts against both organisms.

Keywords: orange peels, total phenolic content, antioxidant, antibacterial

Procedia PDF Downloads 67

Authors: Agoubi Belgacem, Adel Kharroubi

Abstract:

Geothermal groundwater is the main water source to supply various sectors in El Hamma city, southeastern Tunisia. This region was long the destination of thousands of people from Tunisia and neighboring countries for care and bathing. The main objective of this study is to understand the groundwater mineralization origins and factors that control. The second goal is the appraisal of radon in geothermal groundwater in the study area. For this aim, geothermal groundwater was sampled and collected from different locations (thermal baths and deep wells). Physical parameters were measured and major ions were analyzed. Results reveal three water types. The water first type has Na-Mg-Ca-SO4-Cl facies and T>55°C. The second water type dominated by Na-Ca-Cl-SO4 facies with a temperature < 45 °C. However the third water type is dominated by Ca-SO4-Na-Cl-Mg. The three water types may be controlled by depth and geology. The first represent groundwater from deep aquifer (lower cretaceous), the second type was the shallow aquifer and the first is mixed water from deep and shallow water with a temperature ranging from 45 to 55°C. Measured Radon shows that shallow aquifer has a higher 222Rn concentration (677 to 2903 Bq.m-3) than deep water (203 to 1100 Bq.m-3). R-222 in El Hamma thermal aquifer was controlled by structures, porosity and permeability of aquifers. Geostatistical analyses of hydrogeological data and radon activities confirm the vertical flow and communication between deep and shallow aquifers through vertical faults system.

Keywords: Radon-222, geothermal, water, environment, Tunisia

Procedia PDF Downloads 355

Authors: Pratap K. Chhotaray, Jitendriya Swain, Ashok Mishra, Ramesh L. Gardas

Abstract:

Ionic liquids (ILs) are molten salts, consist predominantly of ions and found to be liquid below 100°C. The unparalleled growing interest in ILs is based upon their never ending design flexibility. The use of ILs as a co-solvent in binary as well as a ternary mixture with molecular solvents multifold it’s utility. Since polarity is one of the most widely applied solvent concepts which represents simple and straightforward means for characterizing and ranking the solvent media, its study for a binary mixture of ILs is crucial for its widespread application and development. The primary approach to the assessment of solution phase intermolecular interactions, which generally occurs on the picosecond to nanosecond time scales, is to exploit the optical response of photophysical probe. Pyrene butyric acid (PBA) is used as fluorescence probe due to its high quantum yield, longer lifetime and high solvent polarity dependence of fluorescence spectra. Propylammonium formate (PAF) is the IL used for this study. Both the UV-absorbance spectra and steady state fluorescence intensity study of PBA in different concentration of aqueous PAF, reveals that with an increase in PAF concentration, both the absorbance and fluorescence intensity increases which indicate the progressive solubilisation of PBA. Whereas, near about 50% of IL concentration, all of the PBA molecules get solubilised as there are no changes in the absorbance and fluorescence intensity. Furthermore, the ratio II/IV, where the band II corresponds to the transition from S1 (ν = 0) to S0 (ν = 0), and the band IV corresponds to transition from S1 (ν = 0) to S0 (ν = 2) of PBA, indicates that the addition of water into PAF increases the polarity of the medium. Time domain lifetime study shows an increase in lifetime of PBA towards the higher concentration of PAF. It can be attributed to the decrease in non-radiative rate constant at higher PAF concentration as the viscosity is higher. The monoexponential decay suggests that homogeneity of solvation environment whereas the uneven width at full width at half maximum (FWHM) indicates there might exist some heterogeneity around the fluorophores even in the water-IL mixed solvents.

Keywords: fluorescence, ionic liquid, lifetime, polarity, pyrene butyric acid

Procedia PDF Downloads 454

Authors: D. S. Fardhyanti, Megawati, W. B. Sediawan

Abstract:

Coal tar is a liquid by-product of the process of coal gasification and carbonation. This liquid oil mixture contains various kinds of useful compounds such as aromatic compounds and phenolic compounds. These compounds are widely used as raw material for insecticides, dyes, medicines, perfumes, coloring matters, and many others. This research investigates thermodynamic modelling of liquid-liquid equilibria (LLE) in the separation of phenol from the coal tar by solvent extraction. The equilibria are modeled by ternary components of Wohl, Van Laar, and Three-Suffix Margules models. The values of the parameters involved are obtained by curve-fitting to the experimental data. Based on the comparison between calculated and experimental data, it turns out that among the three models studied, the Three-Suffix Margules seems to be the best to predict the LLE of p-Cresol mixtures for those system.

Keywords: coal tar, phenol, Wohl, Van Laar, Three-Suffix Margules

Procedia PDF Downloads 253

Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

Abstract:

Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

Procedia PDF Downloads 196

Authors: Brinda Sharma, Saakshi S. Sarpotdar

Abstract:

Deep-fat frying is popular standard method that has been studied basically to clarify the complicated mechanisms of fat decomposition at high temperatures and to assess their effects on human health. The aim of this paper is to point out the application of method engineering that has been recently improved our understanding of the fundamental principles and mechanisms concerned at different scales and different times throughout the process: pretreatment, frying, and cooling. It covers the several aspects of deep-fat drying. New results regarding the understanding of the frying method that are obtained as a results of major breakthroughs in on-line instrumentation (heat, steam flux, and native pressure sensors), within the methodology of microstructural and imaging analysis (NMR, MRI, SEM) and in software system tools for the simulation of coupled transfer and transport phenomena. Such advances have opened the approach for the creation of significant information of the behavior of varied materials and to the event of latest tools to manage frying operations via final product quality in real conditions. Lastly, this paper promotes an integrated approach to the frying method as well as numerous competencies like those of chemists, engineers, toxicologists, nutritionists, and materials scientists also as of the occupation and industrial sectors.

Keywords: frying, cooling, imaging analysis (NMR, MRI, SEM), deep-fat frying

Procedia PDF Downloads 427

Authors: Shradha S. Binani, P. S. Bodke, R. V. Joat

Abstract:

Recent decades have witnessed an exponential growth in the field of acoustical parameters and ultrasound on solid, liquid and gases. Ultrasonic propagation parameters yield valuable information regarding the behavior of liquid systems because intra and intermolecular association, dipolar interaction, complex formation and related structural changes affecting the compressibility of the system which in turn produces variations in the ultrasonic velocity. The acoustic and thermo dynamical parameters obtained in ultrasonic study show that ion-solvation is accompanied by the destruction or enhancement of the solvent structure. In the present paper the ultrasonic velocity (v), density (ρ), viscosity(η) have been measured for the pharmacological important compound 2-hydroxy substituted phenyl pyrimidine derivative (2-hydroxy-4-(4’-methoxy phenyl)-6-(2’-hydroxy-4’-methyl-5’-chlorophenyl)pyrimidine) in ethanol as a solvent by using different concentration at constant room temperature. These experimental data have been used to estimate physical parameter like adiabatic compressibility, intermolecular free length, relaxation time, free volume, specific acoustic impedance, relative association, Wada’s constant, Rao’s constant etc. The above parameters provide information in understanding the structural and molecular interaction between solute-solvent in the drug solution with respect to change in concentration.

Keywords: acoustical parameters, ultrasonic velocity, density, viscosity, 2-hydroxy substituted phenyl pyrimidine derivative

Procedia PDF Downloads 466

Authors: Gizem Eser Erdek

Abstract:

This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

Procedia PDF Downloads 74

Authors: F. Adjel, S. Almi, D. Barkat

Abstract:

The synergistic solvent extraction of cobalt (II) from 0.33 mol dm-3 Na2SO4 aqueous solutions with capric acid (HL) in the absence and presence of tri-n-octylphosphine oxide (TOPO) in chloroform at 25°C, has been studied. The extracted species when the capric acid compound was used alone, is CoL2(HL)2. In the presence of TOPO, a remarkable enhancement on the extraction of nickel (II) with 0.02 mol dm-3 capric acid was observed upon the addition of 0.0025 to 0.01 mol dm-3 TOPO in chloroform. From an synergistic extraction- equilibrium study, the synergistic enhancement was ascribed to the adduct formation CoL2(HL)2 n(TOPO). The TOPO-HL interaction strongly influences the synergistic extraction efficiency. The synergistic extraction stoichiometry of cobalt (II) with capric acid and TOPO is studied with the methods of slope analysis. The equilibrium constants were determined.

Keywords: solvent extraction, cobalt (II), capric acid, TOPO, synergism

Procedia PDF Downloads 519

Authors: F. Adjel, C. Bensmail, S. Almi, D. Barkat

Abstract:

The synergistic solvent extraction of cobalt (II) from 0.33 mol dm^-3 Na2SO4 aqueous solutions with capric acid (HL) in the absence and presence of methyl isobutyl cétone (MIBK) in chloroform at 25°C, has been studied. The extracted species when the capric acid compound was used alone, is CoL2(HL)2. In the presence of MIBK, a remarkable enhancement on the extraction of nickel (II) with 0.02 mol dm^-3 capric acid was observed upon the addition of 0.0025 to 0.01 mol dm^-3 MIBK in chloroform. From a synergistic extraction-equilibrium study, the synergistic enhancement was ascribed to the adduct formation CoL2(HL)2 n(MIBK). The MIBK-HL interaction strongly influences the synergistic extraction efficiency. The synergistic extraction stoichiometry of cobalt (II) with capric acid and MIBK is studied with the methods of slope analysis. The equilibrium constants were determined.

Keywords: solvent extraction, cobalt (II), capric acid, MIBK, synergism

Procedia PDF Downloads 486

Authors: A. O. Boyo, A. T. Akinwunmi

Abstract:

The effect of extracting solvent and adjustment of pHs on the stability of Terminalia catappa L. dye-sensitized solar cell was investigated. We introduced ZnO as an alternative to TiO2 in the dye sensitized solar cells (DSSCs) due to its band gap similar to TiO2, higher electron mobility, and flexible procedures of preparations. Dye-sensitized solar cells (DSSCs) based on Terminalia catappa L. was extracted in water (A), ethanol (B) and the mixture of ethanol and water in the ratio 1:1by volume (C). The best performance Solar cells sensitized was from extracts A and achieved up to Jsc 1.51 mAcm−2, Voc 0.75V, FF 0.88 and η 0.63%. We notice that as pHs decreases there is the increase in DSSC efficiency. There is Long period stability in efficiency of the cells prepared using A than in C and a fair stability in efficiency of B cell. The results obtained with extracts B and C confirmed that Ethanol with water could not be considered as a suitable solvent for the extraction of natural dye.

Keywords: zinc oxide, dye-sensitized solar cell, terminalia catappa L., TiO2

Procedia PDF Downloads 399

Authors: Akshita Jindal, Renu Chadha, Maninder Karan

Abstract:

Supramolecular chemistry has gained recent eminence in a flurry of research documents demonstrating the formation of new crystalline forms with potentially advantageous characteristics. Mesalamine (5-amino salicylic acid) belongs to anti-inflammatory class of drugs, is used to treat ulcerative colitis and Crohn’s disease. Unfortunately, mesalamine suffer from poor solubility and therefore very low bioavailability. This work is focused on preparation and characterization of cocrystal of mesalamine with nicotinamide (MNIC) a coformer of GRAS status. Cocrystallisation was achieved by solvent drop grinding in stoichiometric ratio of 1:1 using acetonitrile as solvent and was characterized by various techniques including DSC (Differential Scanning Calorimetry), PXRD (X-ray Powder Diffraction), and FTIR (Fourier Transform Infrared Spectrometer). The co-crystal depicted single endothermic transitions (254°C) which were different from the melting peaks of both drug (288°C) and coformer (128°C) indicating the formation of a new solid phase. Different XRPD patterns and FTIR spectrums for the co-crystals from those of individual components confirms the formation of new phase. Enhancement in apparent solubility study and intrinsic dissolution study showed effectiveness of this cocrystal. Further improvement in pharmacokinetic profile has also been observed with 2 folds increase in bioavailability. To conclude, our results show that application of nicotinamide as a coformer is a viable approach towards the preparation of cocrystals of potential drug molecule having limited solubility.

Keywords: cocrystal, mesalamine, nicotinamide, solvent drop grinding

Procedia PDF Downloads 167

Authors: Dingyang Hu, Dan Liu

Abstract:

DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

Procedia PDF Downloads 96

Authors: Mosaad Attia Elkasaby

Abstract:

The crude oil contains paraffines, aromatics, and asphaltenes in addition to some organic impurities, with increasing demands to reduce the cost of crude oil production, the uses of a pour point depressant is mandatory to maintain good flow rate. The wax materials cause many problems during production, storage, and transport, especially at low temperature, as these waxes tend, at low temperatures, to precipitate on the wall lines, thus leads to the high viscosity of crude oil and impede the flow rate, which represents an additional burden for crude oil pumping system from the place of production to the refinery. There are many ways to solve this problem, including, but not limited to, heat the crude and the use of organic solvents. But one of the most important disadvantages of these methods is the high economic cost. The aim of this innovation is to manufacture some polymeric materials (polymers based on aniline) that are processed locally that can be used as a pour point depressant of crude oil. For the first time, polymer based on aniline is modified and used with a number of organic solvents and tested with solvent (Styrene). It was found that the polymer based on aniline, when modified, had full solubility in styrene, unlike other organic solvent that was used in the past, such as chloroform and toluene. We also used a new solvent (PONA) that is obtained from the process of hydrotreating and separation of straight run naphtha to dissolve polymer based on aniline as a pour point depressant of crude oil. This innovative include studies conducted on highly paraffinic crude oil (C.O.1 and C.O.2). On using concentration (2500 ppm) of polymer based on aniline, the pour point of crude oil has decreased from +33 to - 9°C in case of crude oil (C.O.1) and from + 42 to – 6°C in case crude oil (C.O.2) at the same concentration.

Keywords: PPD, aniline, paraffinic crude oils, polymers

Procedia PDF Downloads 88

Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

Abstract:

In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

Procedia PDF Downloads 97

Authors: Nouria Bouchikhi, Soufiane Bedjaoui, C. Tewfik Bouchaour, Lamia Alachaher Bedjaoui, Ulrich Maschke

Abstract:

Swelling properties and phase diagrams of binary systems composed of liquid crystalline networks and a low molecular mass liquid crystal (LMWLC) have been investigated. The networks were prepared by ultraviolet (UV) irradiation of reactive mixtures including a monomer, a cross-linking agent and a photo-initiator. These networks were prepared using two cross-linking agents: 1,6 hexanedioldiacrylate (HDDA) and a mesogenic acrylic acid 6-(4’-(6-acryloyloxy-hexyloxy) biphenyl-4-yl oxy) hexyl ester (AHBH). The obtained dry networks were characterized by differential scanning calorimetry, and immersed in an excess of a LMWLC solvent 4-cyano-4’-pentylbiphenyl (5CB), forming polymer gels. A detailed study by polarized optical microscopy allowed to determine the swelling degree of the gels and to follow the phase behavior of the solvent inside the polymer matrix in a wide range of temperature. It has been found that the gels undergo a sharp decrease of their swelling degree in response to an infinitesimal change of temperature. This finding adds new and interesting aspects on the actuators applications. We have subsequently explored the effect of different parameters on volume phase transition of these liquid crystalline materials. Such as the cross-linking density (CD), a nature of cross-linking agent and the photo initiator concentration.

Keywords: cross-linking density, liquid crystalline elastomers, phase diagrams, swelling

Procedia PDF Downloads 328

Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

Procedia PDF Downloads 193

Authors: Ene Rosemary Ndidiamaka, Nwangwu Florence Chinyere

Abstract:

The physiochemical properties of the melon seed oil was studied to determine its potentials as viable feed stock for biodisel production. The melon seed was extracted by solvent extraction using n-hexane as the extracting solvent. In this research, methanol was the alcohol used in the production of biodiesel, although alcohols like ethanol, propanol may also be used. Sodium hydroxide was employed for the catalysis. The melon seed oil was characterized for specific gravity, pH, ash content, iodine value, acid value, saponification value, peroxide value, free fatty acid value, flash point, viscosity, and refractive index using standard methods. The melon seed oil had very high oil content. Specific gravity and flash point of the oil is satisfactory. However, moisture content of the oil exceeded the stipulated ASRTM standard for biodiesel production. The overall results indicates that the melon seed oil is suitable for single-stage transesterification process to biodiesel production.

Keywords: biodiesel, catalyst, melon seed, transesterification

Procedia PDF Downloads 362

Authors: R. A. Akande, M. L. Mnisi

Abstract:

Introduction: Aloe vera (Aloe barbadensis miller) flowers are carried in a large candelabra-like flower-head. Aloe barbadensis miller has been known as a traditional herbal medicine for the treatment of many diseases and sicknesses mainly for skin conditions such as sunburns, cold sores and frostbite. It is also used as a fresh food preservative. The main objective of this study is to determine the antioxidant activity of Aloe barbadensis miller. Methodology: The plant material (3g) was separately extracted with 30 mL of solvent with varying polarities (methanol and ethyl acetate)(technical grade, Merck) in 50ml polyester centrifuge tubes. The tubes was be shaken for 30 minutes on a linear shaker and left over night. The supernatant was filtered using a Whitman No. 1 filter paper before being transferred into pre-weighed glass containers. The solvent was allowed to evaporate under a fan in a room to quantify extraction efficacy. The, tin layer chromatography(TLC) plates were prepared and Pasteur pipette was used for spotting each extractant (methanol and ethyl acetate) on the TLC plates and the plate was developed in saturated TLC tank .and dipped in vanillin sulphuric acid mixture and heated at 110 to detect separate compound .and dipped in DDPH in methanol to detect antioxidant. Expected contribution to knowledge: It was observed that different compounds which interact differently with different solvent such as methanol, ethyl acetate having difference polarities were observed. The yellow spots also observed from the plate dipped in DDPH indicate that Aloe barbadensis miller has antioxidant.

Keywords: antioxidant activity, Aloe barbadensis miller, tin layer chromatography, DDPH

Procedia PDF Downloads 445

Authors: Nilay Tanrıver, Birgül Benli, Nilgün Kızılcan

Abstract:

Polysulfone (PSU) is a specialty engineering polymer having various industrial applications. PSU is especially used in waste water treatment membranes due to its good mechanical properties, structural and chemical stability. But it is a hydrophobic material and therefore its surface aim to pollute easily. In order to resolve this problem and extend the properties of membrane, PSU surface is rendered hydrophilic by addition of the sepiolite nanofibers. Sepiolite is one of the natural clays, which is a hydrate magnesium silicate fiber, also one of the well known layered clays of the montmorillonites where has several unique channels and pores within. It has also moisture durability, strength and low price. Sepiolite channels give great capacity of absorption and good surface properties. In this study, nanocomposites of commercial PSU and Sepiolite were prepared by solvent mixing method. Different organic solvents and their mixtures were used. Rheological characteristics of PSU-Sepiolite solvent mixtures were analyzed, the solubility of nanocomposite content in those mixtures were studied.

Keywords: nanocomposite, polysulfone, rheology, sepiolite, solution mixing

Procedia PDF Downloads 420

Authors: Megha Gupta, Nupur Prakash

Abstract:

Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

Procedia PDF Downloads 195

Authors: M. Abdoul-Hakim, a. Zeroual, H. Garmes

Abstract:

In a route for the preparation of 1,4-benzoxazepine fused to benzimidazole, the use of 2-(2-methoxyphenyl)-benzimidazole or styrene-derived N-tosylaziridine does not give the desired products. On this basis, we theoretically studied this reaction using DFT at the B3LYP/6-31+G(d) level. The analysis of the results shows a preferential nucleophilic attack of 2-(2-fluorophenyl)-benzimidazole on the terminal carbon atom of the Alkylepoxides and on the substituted carbon of N-tosylaziridine. Taking into account the solvent effect (DMF) makes the reactions spontaneous for the opening of epoxides and N-tosylaziridine and disfavors the intramolecularnucleophilic aromatic substitution reaction step of the products of the attack of 2-(2-methoxyphenyl)benzimidazole on an epoxide and those of the opening of N-tosylaziridine, which is consistent with the experiment.

Keywords: alkylepoxides, 4-benzoxazepine fused to benzimidazole imine, benzonitrile N-oxide, DFT, intramolecular nucleophilic aromatic substitution, N-tosyl aziridine

Procedia PDF Downloads 135

Authors: Joon Y. Lee, Seung H. Shin, Ho H. Chun, Wan K. Jo

Abstract:

Poly vinyl acetate (PVA)-based titania (TiO2)–carbon nanotube composite nanofibers (PVA-TCCNs) with various PVA-to-solvent ratios and PVA-based TiO2 composite nanofibers (PVA-TN) were synthesized using an electrospinning process, followed by thermal treatment. The photocatalytic activities of these nanofibers in the degradation of airborne monocyclic aromatics under visible-light irradiation were examined. This study focuses on the application of these photocatalysts to the degradation of the target compounds at sub-part-per-million indoor air concentrations. The characteristics of the photocatalysts were examined using scanning electron microscopy, X-ray diffraction, ultraviolet-visible spectroscopy, and Fourier-transform infrared spectroscopy. For all the target compounds, the PVA-TCCNs showed photocatalytic degradation efficiencies superior to those of the reference PVA-TN. Specifically, the average photocatalytic degradation efficiencies for benzene, toluene, ethyl benzene, and o-xylene (BTEX) obtained using the PVA-TCCNs with a PVA-to-solvent ratio of 0.3 (PVA-TCCN-0.3) were 11%, 59%, 89%, and 92%, respectively, whereas those observed using PVA-TNs were 5%, 9%, 28%, and 32%, respectively. PVA-TCCN-0.3 displayed the highest photocatalytic degradation efficiency for BTEX, suggesting the presence of an optimal PVA-to-solvent ratio for the synthesis of PVA-TCCNs. The average photocatalytic efficiencies for BTEX decreased from 11% to 4%, 59% to 18%, 89% to 37%, and 92% to 53%, respectively, when the flow rate was increased from 1.0 to 4.0 L min1. In addition, the average photocatalytic efficiencies for BTEX increased 11% to ~0%, 59% to 3%, 89% to 7%, and 92% to 13% , respectively, when the input concentration increased from 0.1 to 1.0 ppm. The prepared PVA-TCCNs were effective for the purification of airborne aromatics at indoor concentration levels, particularly when the operating conditions were optimized.

Keywords: mixing ratio, nanofiber, polymer, reference photocatalyst

Procedia PDF Downloads 372

Authors: F. Adjel, S. Almi, D. Barkat

Abstract:

The synergistic solvent extraction of nickel ion from 0.33 mol dm^-3 Na2SO4 aqueous solutions with capric acid (HL) in the absence and presence of Tri-n-octylphosphine oxide (TOPO) in chloroform at 25°C, has been studied. The extracted species when the capric acid compound was used alone, is NiL2 and NiL2(HL). In the presence of TOPO, a remarkable enhancement on the extraction of nickel (II) with 0.02 mol dm^-3 capric acid was observed upon the addition of 0.00125 and 0.0025 mol dm^-3 TOPO in chloroform. From a synergistic extraction- equilibrium study, the synergistic enhancement was ascribed to the adduct formation NiL2(TOPO) and NiL2(HL)(TOPO). The TOPO-HL interaction strongly influences the synergistic extraction efficiency. The synergistic extraction stoichiometry of nickel (II) with capric acid and TOPO is studied with the methods of slope analysis. The equilibrium constants were determined.

Keywords: solvent extraction, nickel(II), capric acid, TOPO, synergism

Procedia PDF Downloads 592