Search results for: artificial air storage reservoir

Authors: Ukpaka Paschal

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Margret Boden argues that a creative product is one that is new, surprising, and valuable as a result of the combination, exploration, or transformation involved in producing it. Boden uses examples of artificial intelligence systems that fit all of these criteria and argues that real creativity involves autonomy, intentionality, valuation, emotion, and consciousness. This paper provides an analysis of all these elements in order to try to understand whether they are sufficient to account for creativity, especially human creativity. This paper focuses on Generative Adversarial Networks (GANs), which is a class of artificial intelligence algorithms that are said to have disproved the common perception that creativity is something that only humans possess. This paper will then argue that Boden’s listed properties of creativity, which capture the creativity exhibited by GANs, are not sufficient to account for human creativity, and this paper will further identify “first-person phenomenological experience” as an essential property of human creativity. The rationale behind the proposed essential property is that if creativity involves comprehending our experience of the world around us into a form of self-expression, then our experience of the world really matters with regard to creativity.

Keywords: artificial intelligence, creativity, GANs, first-person experience

Procedia PDF Downloads 135

Authors: Haroon Jamshaid Qazi, Namrah Wahid, Sanaullah Iqbal, Raheel Suleman

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The aim of the current study was to evaluate the antioxidant and antimicrobial potential of Rutin in cheddar cheese. The study was conducted by adding the Rutin in the cheddar cheese in different concentrations according to experimental design, i.e., T1 (20 ppm Rutin), T2 (40 ppm Rutin), T3 (60 ppm Rutin), T4 (80 ppm Rutin). BHT was taken as a positive control at a concentration of 200 ppm, and negative control had neither Rutin nor BHT. The ripening time for cheeses was 90 days at a temperature of 8°C. The results of the various antioxidants assays (Total phenolic contents (TPC) and Antioxidant activity (AA), with storage stability tests (Anisidine value (AV) and Thiobarbituric acid value (TBARS)) performed during different storage intervals 0, 30, 60 and 90 days exhibited that AA in linoleic acid and TPC were significantly (p < 0.05) increased by the addition of rutin to cheese at all concentrations. Moreover, significant reduction in the TBARS values was also observed during the storage period. Rutin also showed a good potential to inhibit the microbial proliferation in the treated samples of cheese. There was a significant decreasing trend seen in total plate count and yeasts and molds count. The sensorial attributes i.e., color, flavor, odor and overall acceptability were increased after adding Rutin to cheddar cheese.

Keywords: cheddar cheese, Rutin, antioxidant, antimicrobial

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Authors: Harris Niavis, Dimitra Politaki

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The manufacturing industry was collecting vast amounts of data for monitoring product quality thanks to the advances in the ICT sector and dedicated IoT infrastructure is deployed to track and trace the production line. However, industries have not yet managed to unleash the full potential of these data due to defective data collection methods and untrusted data storage and sharing. Blockchain is gaining increasing ground as a key technology enabler for Industry 4.0 and the smart manufacturing domain, as it enables the secure storage and exchange of data between stakeholders. On the other hand, AI techniques are more and more used to detect anomalies in batch and time-series data that enable the identification of unusual behaviors. The proposed scheme is based on smart contracts to enable automation and transparency in the data exchange, coupled with anomaly detection algorithms to enable reliable data ingestion in the system. Before sensor measurements are fed to the blockchain component and the smart contracts, the anomaly detection mechanism uniquely combines artificial intelligence models to effectively detect unusual values such as outliers and extreme deviations in data coming from them. Specifically, Autoregressive integrated moving average, Long short-term memory (LSTM) and Dense-based autoencoders, as well as Generative adversarial networks (GAN) models, are used to detect both point and collective anomalies. Towards the goal of preserving the privacy of industries' information, the smart contracts employ techniques to ensure that only anonymized pointers to the actual data are stored on the ledger while sensitive information remains off-chain. In the same spirit, blockchain technology guarantees the security of the data storage through strong cryptography as well as the integrity of the data through the decentralization of the network and the execution of the smart contracts by the majority of the blockchain network actors. The blockchain component of the Data Traceability Software is based on the Hyperledger Fabric framework, which lays the ground for the deployment of smart contracts and APIs to expose the functionality to the end-users. The results of this work demonstrate that such a system can increase the quality of the end-products and the trustworthiness of the monitoring process in the smart manufacturing domain. The proposed AI-enabled data traceability software can be employed by industries to accurately trace and verify records about quality through the entire production chain and take advantage of the multitude of monitoring records in their databases.

Keywords: blockchain, data quality, industry4.0, product quality

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Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Karl-Erich Lindenschmidt, Apurba Das, Joel Trudell, Keanne Russell

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In this presentation, we explore options for mitigating ice-jam flooding along the Athabasca River in western Canada. Not only flood hazard, expressed in this case as the probability of flood depths and extents being exceeded, but also flood risk, in which annual expected damages are calculated. Flood risk is calculated, which allows a cost-benefit analysis to be made so that decisions on the best mitigation options are not based solely on flood hazard but also on the costs related to flood damages and the benefits of mitigation. The river ice model is used to simulate extreme ice-jam flood events with which scenarios are run to determine flood exposure and damages in flood-prone areas along the river. We will concentrate on three mitigation options – the placement of a dike, artificial breakage of the ice cover along the river, the installation of an ice-control structure, and the construction of a reservoir. However, any mitigation option is not totally failsafe. For example, dikes can still be overtopped and breached, and ice jams may still occur in areas of the river where ice covers have been artificially broken up. Hence, for all options, it is recommended that zoning of building developments away from greater flood hazard areas be upheld. Flood mitigation can have a negative effect of giving inhabitants a false sense of security that flooding may not happen again, leading to zoning policies being relaxed. (Text adapted from Lindenschmidt [2022] "Ice Destabilization Study - Phase 2", submitted to the Regional Municipality of Wood Buffalo, Alberta, Canada)

Keywords: ice jam, flood hazard, flood risk river ice modelling, flood risk

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Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

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Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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Authors: Marisa Almeida AraúJo

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Artificial intelligence (AI) tools are being used (including in the criminal justice system) and becomingincreasingly popular. The many questions that these (future) super-beings pose the neuralgic center is rooted in the (old) problematic between rationality and morality. For instance, if we follow a Kantian perspective in which morality derives from AI, rationality will also surpass man in ethical and moral standards, questioning the nature of mind, the conscience of self and others, and moral. The recognition of superior intelligence in a non-human being puts us in the contingency of having to recognize a pair in a form of new coexistence and social relationship. Just think of the humanoid robot Sophia, capable of reasoning and conversation (and who has been recognized for Saudi citizenship; a fact that symbolically demonstrates our empathy with the being). Machines having a more intelligent mind, and even, eventually, with higher ethical standards to which, in the alluded categorical imperative, we would have to subject ourselves under penalty of contradiction with the universal Kantian law. Recognizing the complex ethical and legal issues and the significant impact on human rights and democratic functioning itself is the goal of our work.

Keywords: ethics, artificial intelligence, legal rules, principles, philosophy

Procedia PDF Downloads 197

Authors: Remo Waser, Simon Maranda, Anastasia Stamatiou, Ludger J. Fischer, Joerg Worlitschek

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In latent heat storage, a phase change material (PCM) is used to store thermal energy. The heat transfer rate during solidification is limited and considered as a key challenge in the development of latent heat storages. Thus, finned heat exchangers (HEX) are often utilized to increase the heat transfer rate of the storage system. In this study, a new modeling approach to calculating the heat transfer rate in latent thermal energy storages with complex HEX geometries is presented. This model allows for an optimization of the HEX design in terms of costs and thermal performance of the system. Modeling solidification processes requires the calculation of time-dependent heat conduction with moving boundaries. Commonly used computational fluid dynamic (CFD) methods enable the analysis of the heat transfer in complex HEX geometries. If applied to the entire storage, the drawback of this approach is the high computational effort due to small time steps and fine computational grids required for accurate solutions. An alternative to describe the process of solidification is the so-called temperature-based approach. In order to minimize the computational effort, a quasi-stationary assumption can be applied. This approach provides highly accurate predictions for tube heat exchangers. However, it shows unsatisfactory results for more complex geometries such as finned tube heat exchangers. The presented simulation model uses a temporal and spatial discretization of heat exchanger tube. The spatial discretization is based on the smallest possible symmetric segment of the HEX. The heat flow in each segment is calculated using finite volume method. Since the heat transfer fluid temperature can be derived using energy conservation equations, the boundary conditions at the inner tube wall is dynamically updated for each time step and segment. The model allows a prediction of the thermal performance of latent thermal energy storage systems using complex HEX geometries with considerably low computational effort.

Keywords: modelling of solidification, finned tube heat exchanger, latent thermal energy storage

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Authors: Sreejith Gopinath, Aspen Olmsted

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This paper is based on our previous work that proposed a risk-transference-based architecture for healthcare systems to store sensitive data outside the system boundary, rendering the system unattractive to would-be bad actors. This architecture also allows a compromised system to be abandoned and a new system instance spun up in place to ensure business continuity without paying a ransom or engaging with a bad actor. This paper delves into the details of various attacks we simulated against the prototype system. In the paper, we discuss at length the time and computational costs associated with storing and retrieving data in the prototype system, abandoning a compromised system, and setting up a new instance with existing data. Lastly, we simulate some analytical workloads over the data stored in our specialized data storage system and discuss the time and computational costs associated with running analytics over data in a specialized storage system outside the system boundary. In summary, this paper discusses the total costs of data storage, access, and analytics incurred with the proposed architecture.

Keywords: cybersecurity, healthcare, ransomware, resilience, risk transference

Procedia PDF Downloads 132

Authors: Hsu-Yung Cheng, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Chang Yu, Ya-Sheng Liu

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Photovoltaics modules are usually not installed horizontally to avoid water or dust accumulation. However, the measured irradiance data on tilted surfaces are rarely available since installing pyranometers with various tilt angles induces high costs. Therefore, estimating solar irradiance on tilted surfaces is an important research topic. In this work, artificial neural networks (ANN) are utilized to construct the transfer model to estimate solar irradiance on tilted surfaces. Instead of predicting tilted irradiance directly, the proposed method estimates the differences between the horizontal irradiance and the irradiance on a tilted surface. The outputs of the ANNs in the proposed design are differential values. The experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy compared to ANNs that estimate the titled irradiance directly.

Keywords: photovoltaics, artificial neural networks, tilted irradiance, solar energy

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Authors: O. Guellati, A. Harat, F. Djefaflia, N. Habib, A. Nait-Merzoug, J. El Haskouri, D. Momodu, N. Manyala, D. Bégin, M. Guerioune

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We report a facile and low-cost free-template synthesis method was used to synthesize mesoporous smart multifunctional nanohybrids based on Graphene/PANI nanofibers micro/nanostructures with very interesting physic-chemical properties and faradic electrochemical behavior of these products was investigated. These nanohybrid products have been characterized quantitatively and qualitatively using different techniques, such as XRD / FTIR, Raman, XPS spectroscopy, Field Emission SEM and High-Resolution TEM microscopy, BET textural analysis, electrochemical measurements (CV, CD, EIS). Moreover, the electrochemical measurements performed in a 6 M KOH aqueous electrolyte depicted excellent electrochemical performance ascribed to the optimized composition of hydroxides et PANI nanofibers. An exceptionally notable specific capacitance between 800  and 2000 F. g-1 was obtained at 5  mV. s-1 scan rate for these synthesized products depends on the optimized growth conditions. We found much better nanohybrids by reinforcing hydroxides or conduction polymer nanofibers with carbonaceous nanomaterials depicting their potential as suitable materials for energy storage devices.

Keywords: nanohybrid materials, conducting polymers, carbonaceous nanomaterials, supercapacitors, energy storage

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

Procedia PDF Downloads 692

Authors: Ting Pan, Jiacheng Wang, Pengcheng Lin, Ying Chen, Songping Mo

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Phase change materials (PCMs) are widely used in latent heat thermal energy storage because of their good properties such as high energy storage density and constant heat-storage/release temperature. Microencapsulation techniques can prevent PCMs from leaking during the liquid-solid phase transition and enhance thermal properties. This technique has been widely applied in architectural materials, thermo-regulated textiles, aerospace fields, etc. One of the most important processes during the synthesis of microcapsules is to form a stable emulsion of the PCM core and reactant solution for the formation of the shell of the microcapsules. The use of surfactants is usually necessary for the formation of a stable emulsion system because of the difference in hydrophilia/lipophilicity of the PCM and the solvent. Unfortunately, the use of surfactants may cause pollution to the environment. In this study, modified kaolinite was used as an emulsion stabilizer for the microencapsulation of octodecane as PCM. Microcapsules were synthesized by phase inversion emulsification method, and the shell of polymethyl methacrylate (PMMA) was formed through free radical polymerization. The morphologies, crystalloid phase, and crystallization properties of microcapsules were investigated using scanning electron microscopy (SEM), X-ray diffractometer (XRD), and Fourier transforms infrared spectrometer (FTIR). The thermal properties and thermal stability were investigated by a differential scanning calorimeter (DSC) and a thermogravimetric analyzer (TG). The FT-IR, XRD results showed that the octodecane was well encapsulated in the PMMA shell. The SEM results showed that the microcapsules were spheres with an average size of about 50-100nm. The DSC results indicated that the latent heat of the microcapsules was 152.64kJ/kg and 164.23kJ/kg. The TG results confirmed that the microcapsules had good thermal stability due to the PMMA shell. Based on the results, it can be concluded that the modified kaolinite can be used as an emulsifier for the synthesis of PCM microcapsules, which is valid for reducing part of the possible pollution caused by the utilization of surfactants.

Keywords: kaolinite, microencapsulation, PCM, thermal energy storage

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

Procedia PDF Downloads 221

Authors: Yichao Liang, Biye Chen, Xiang Li, Steven R. Dimler

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This study investigated the effects of adding calcium and magnesium chloride on heat and storage stability of milk protein concentrate-soy protein isolate (8:2 respectively) mixtures containing 10% w/w total protein subjected to the in-container sterilization (115 °C x 15 min). The particle size does not change when emulsions are heated at pH between 6.7 and 7.3 irrespective of the mixed protein ratio. Increasing concentration of divalent cation salts resulted in an increase in protein particle size, dry sediment formation and sediment height and a decrease in pH, heat stability and hydration in milk protein concentrate-soy protein isolate mixtures solutions on sterilization at 115°C. Fortification of divalent cation salts in milk protein concentrate-soy protein isolate mixture solutions resulted in an accelerated protein sedimentation and two unique sediment regions during accelerated storage stability testing. Moreover, the heat stability decreased upon sterilization at 115°C, with addition of MgCl₂ causing a greater increase in sedimentation velocity and compressibility than CaCl₂. Increasing pH value of protein milk concentrate-soy protein isolate mixtures solutions from 6.7 to 7.2 resulted in an increase in viscosity following the heat treatment. The study demonstrated that the type and concentration of divalent cation salts used strongly impact heat and storage stability of milk protein concentrate-soy protein isolate mixture nutritional beverages.

Keywords: divalent cation salts, heat stability, milk protein concentrate, soy protein isolate, storage stability

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Authors: Igor A. Bessmertny, Bidru C. Enkomaryam

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Active involving patients and communities in health decisions can improve both people’s health and the healthcare system. Adopting artificial intelligence can lead to more accurate and complete patient record management. This review aims to identify the current state of researches conducted using artificial intelligence techniques to improve patient engagement and wellbeing, medical domains used in patient engagement context, and lastly, to assess opportunities and challenges for patient engagement in the wellness process. A search of peer-reviewed publications, reviews, conceptual analyses, white papers, author’s manuscripts and theses was undertaken. English language literature published in 2013– 2022 period and publications, report and guidelines of World Health Organization (WHO) were also assessed. About 281 papers were retrieved. Duplicate papers in the databases were removed. After application of the inclusion and exclusion criteria, 41 papers were included to the analysis. Patient counseling in preventing adverse drug events, in doctor-patient risk communication, surgical, drug development, mental healthcare, hypertension & diabetes, metabolic syndrome and non-communicable chronic diseases are implementation areas in healthcare where patient engagement can be implemented using artificial intelligence, particularly machine learning and deep learning techniques and tools. The five groups of factors that potentially affecting patient engagement in safety are related to: patient, health conditions, health care professionals, tasks and health care setting. Active involvement of patients and families can help accelerate the implementation of healthcare safety initiatives. In sub-Saharan Africa, using digital technologies like artificial intelligence in patient engagement context is low due to poor level of technological development and deployment. The opportunities and challenges available to implement patient engagement strategies vary greatly from country to country and from region to region. Thus, further investigation will be focused on methods and tools using the potential of artificial intelligence to support more simplified care that might be improve communication with patients and train health care professionals.

Keywords: artificial intelligence, patient engagement, machine learning, patient involvement

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Authors: Eronini Iheanyi Umez-Eronini

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Most studies and existing implementations of compressed air energy storage (CAES) coupled with a wind farm to overcome intermittency and variability of wind power are based on bulk or centralized CAES plants. A dynamic model of a hybrid wind farm with wind turbines and distributed CAES, consisting of air storage tanks and compressor and expander trains at each wind turbine station, is developed and simulated in MATLAB. An ad hoc supervisory controller, in which the wind turbines are simply operated under classical power optimizing region control while scheduling power production by the expanders and air storage by the compressors, including modulation of the compressor power levels within a control range, is used to regulate overall farm power production to track minute-scale (3-minutes sampling period) TSO absolute power reference signal, over an eight-hour period. Simulation results for real wind data input with a simple wake field model applied to a hybrid plant composed of ten 5-MW wind turbines in a row and ten compatibly sized and configured Diabatic CAES stations show the plant controller is able to track the power demand signal within an error band size on the order of the electrical power rating of a single expander. This performance suggests that much improved results should be anticipated when the global D-CAES control is combined with power regulation for the individual wind turbines using available approaches for wind farm active power control. For standalone power plant fuel electrical efficiency estimate of up to 60%, the round trip electrical storage efficiency computed for the distributed CAES wherein heat generated by running compressors is utilized in the preheat stage of running high pressure expanders while fuel is introduced and combusted before the low pressure expanders, was comparable to reported round trip storage electrical efficiencies for bulk Adiabatic CAES.

Keywords: hybrid wind farm, distributed CAES, diabatic CAES, active power control, dynamic modeling and simulation

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Authors: Sonia Anand Knowlton

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There are some cases where we as a society feel deeply uncomfortable with the use of Artificial Intelligence (AI) tools in the judicial decision-making process, and justifiably so. A perfect example is COMPAS, an algorithmic model that predicts recidivism rates of offenders to assist in the determination of their bail conditions. COMPAS turned out to be extremely racist: it massively overpredicted recidivism rates of Black offenders and underpredicted recidivism rates of white offenders. At the same time, there are certain uses of AI in the judicial decision-making process that many would feel more comfortable with and even support. Take, for example, a “super-breathalyzer,” an (albeit imaginary) tool that uses AI to deliver highly detailed information about the subject of the breathalyzer test to the legal decision-makers analyzing their drunk-driving case. This article evaluates the point at which a judge’s use of AI tools begins to undermine the public’s trust in the administration of justice. It argues that the answer to this question depends on whether the AI tool is in a role in which it must perform a moral evaluation of a human being.

Keywords: artificial intelligence, judicial reasoning, morality, technology, algorithm

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Authors: Antonella Cartoni, Mattea Carmen Castrovilli

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A biosensor for lactate detection has been developed using an environmentally friendly approach. The biosensor is based on lactate oxidase (LOX) and has remarkable capabilities for reuse and storage at room temperature. The manufacturing technique employed is ambient electrospray deposition (ESD), which enables efficient and sustainable immobilization of the LOX enzyme on a cost-effective com-mercial screen-printed Prussian blue/carbon electrode (PB/C-SPE). The study demonstrates that the ESD technology allows the biosensor to be stored at ambient pressure and temperature for extended periods without affecting the enzymatic activity. The biosensor can be stored for up to 90 days without requiring specific storage conditions, and it can be reused for up to 24 measurements on both freshly prepared electrodes and electrodes that are three months old. The LOX-based biosensor exhibits a lin-ear range of lactate detection between 0.1 and 1 mM, with a limit of detection of 0.07±0.02 mM. Ad-ditionally, it does not exhibit any memory effects. The immobilization process does not involve the use of entrapment matrices or hazardous chemicals, making it environmentally sustainable and non-toxic compared to current methods. Furthermore, the application of a electrospray deposition cycle on previously used biosensors rejuvenates their performance, making them comparable to freshly made biosensors. This highlights the excellent recycling potential of the technique, eliminating the waste as-sociated with disposable devices.

Keywords: green friendly, reuse, storage performance, immobilization, matrix-free, electrospray deposition, biosensor, lactate oxidase, enzyme

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Authors: María Cárdenas Gaudry, Gonzalo Ramces Fano Miranda

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Sediment removal is very important in the purification of water, not only for reasons of visual perception but also because of its association with odor and taste problems. The Cuchoquesera reservoir, which is in the Andean region of Ayacucho (Peru) at an altitude of 3,740 meters above sea level, visually presents suspended particles and organic impurities indicating that it contains water of dubious quality to deduce that it is suitable for direct consumption of human beings. In order to quantitatively know the degree of impurities, water quality monitoring was carried out from February to August 2018, in which four sampling stations were established in the reservoir. The selected measured parameters were electrical conductivity, total dissolved solids, pH, color, turbidity, and sludge volume. The indicators of the studied parameters exceed the permissible limits except for electrical conductivity (190 μS/cm) and total dissolved solids (255 mg/L). In this investigation, the best combination and the optimal doses of reagents were determined that allowed the removal of sediments from the waters of the Cuchoquesera reservoir, through the physicochemical process of coagulation-flocculation. In order to improve this process during the rainy season, six combinations of reagents were evaluated, made up of three coagulants (ferric chloride, ferrous sulfate, and aluminum sulfate) and two natural flocculants: prickly pear powder (Opuntia ficus-indica) and tara gum (Caesalpinia spinoza). For each combination of reagents, jar tests were developed following the central composite experimental design (CCED), where the design factors were the doses of coagulant and flocculant and the initial turbidity. The results of the jar tests were adjusted to mathematical models, obtaining that to treat the water from the Cuchoquesera reservoir, with a turbidity of 150 UTN and a color of 137 U Pt-Co, 27.9 mg/L of the coagulant aluminum sulfate with 3 mg/L of the natural tara gum flocculant to produce a purified water quality of 1.7 UTN of turbidity and 3.2 U Pt-Co of apparent color. The estimated cost of the dose of coagulant and flocculant found was 0.22 USD/m³. This is how “grey-green” technologies can be used as a combination in nature-based solutions in water treatment, in this case, to achieve potability, making it more sustainable, especially economically, if green technology is available at the site of application of the nature-based hybrid solution. This research is a demonstration of the compatibility of natural coagulants/flocculants with other treatment technologies in the integrated/hybrid treatment process, such as the possibility of hybridizing natural coagulants with other types of coagulants.

Keywords: prickly pear powder, tara gum, nature-based solutions, aluminum sulfate, jar test, turbidity, coagulation, flocculation

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Authors: Poonam Magu, Kumud Khanna, P. Seetharaman

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A kitchen is a major workplace in any home. A large variety of tasks, mainly related to meal preparation is performed here. The types of activities performed are varied and vast. In a way, it is the activities performed that determine the way the space within the kitchen is going to be utilised. A study was conducted in 510 Indian kitchens belonging to middle-income group families living in Delhi. It was conducted in three phases. In the first phase, 510 non-working homemakers were interviewed and questions pertaining to their personal characteristics, meal preparation and physical aspects related to the kitchen were asked. In the second phase, the technique of Path Process Chart was developed. Subsequently, a sub-sample of 50 homemakers was selected from the larger group. The activity of meal preparation was carried out by the homemakers themselves in their kitchens. A time and motion study was conducted using the technique of Path Process Chart. The results were analysed using the appropriate analysis sheets and conclusions were drawn. It was found that the entire kitchen and more specifically, the counter had been 'divided' into a number of workplaces. These workplaces were being used either for performing operations or for the purpose of storage. In many cases, it was used for both. On the whole, in 50 kitchens studied, 21 workplaces were identified which were used for performing operations related to meal preparation and 17 for storage It was also observed that almost the entire kitchen was used for the purpose of storage of some item or the other. The major workplaces where operations were performed were the range, workplaces to the right and left of range and sink and workplaces to the right or left of the sink. The same workplaces were also used for the purpose of storage. There were some workplaces outside the kitchen too, which were used for operations or storage. These were the dining table, courtyard or balcony, bedroom cupboard. On the whole, the range centre and the sink centre were found to be the two most important centres in an Indian kitchen belonging to urban middle-income group family.

Keywords: kitchen, workplace, meal preparation, path process chart

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Authors: Anubhav Thakur, Karam Chand Dhiman

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Storage experiment was conducted to study the effect of polymer, fungicides and insecticide on seed quality parameters and storability in wheat. The experimental material consisted of carry over wheat seeds (variety HPW- 155) of rabi 2017 - 18. The observations were recorded bimonthly on parameters viz; germination (%), seedling length (cm), dry weight (g), vigour index - I, vigour - II, speed of germination, field emergence (%), 100 seed weight (g) for 12 months of storage. All parameters declined with the advancement in storage period. The results showed that seeds treated with polymer + vitavax 200 @ 2 g/kg of seed recorded higher germination percentage (95.00 %), seedling length (17.58 cm), seedling dry weight (0.0138 g), vigour index - I (1670) & vigour - II (1.311), speed of germination (19.98), 100 seed weight (5.54 g) and field emergence (87.33 %) which was at par with vitavax 200 @ 2 g/kg of seed, over untreated control (T1). So it can be concluded that for maintain seed quality and enhancing storability, seed of wheat can either be treated with polymer @ 3 ml/kg of seed + vitavax 200 @ 2 g/kg of seed or vitavax 200 @ 2 g/kg of seed.

Keywords: wheat, seed treatment, storability, seed quality

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Authors: Carlos Alberto Ríos-Reyes, Efraín Casadiego-Quintero

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We propose a generalized workflow for mineralogy investigation of unconventional reservoirs using multi-scale imaging and pore-scale analyses. This workflow can be used for the integral evaluation of these resources. The Cretaceous La Luna Formation´s mudstones in the Middle Magdalena Valley Basin (Colombia) inherently show a heterogeneous pore system with organic and inorganic pores. For this reason, it is necessary to carry out the integration of high resolution 2D images of mapping by conventional petrography, scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy to describe their organic and inorganic porosity to understand the transport mechanism through pores. The analyzed rocks show several pore types, including interparticle pores, organoporosity, intraparticle pores, intraparticle pores, and microchannels and/or microfractures. The existence of interconnected pores in pore system of these rocks promotes effective pathways for primary gas migration and storage space for residual hydrocarbons in mudstones, which is very useful in this type of gas reservoirs. It is crucial to understand not only the porous system of these rocks and their mineralogy but also to project the gas flow in order to design the appropriate strategies for the stimulation of unconventional reservoirs. Keywords: mudstones; La Luna Formation; gas storage; migration; hydrocarbon.

Keywords: mudstones, La luna formation, gas storage, migration, hydrocarbon

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Authors: Luís Miguel Estevão Cristóvão, Constâncio Augusto Machanguana, Fernando Chichango, Salvador Grande

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Sub-Saharan African nations depend greatly on agriculture, a sector mainly marked by low production. Most of the farmers live in rural areas and employ basic labor-intensive technologies that lead to time inefficiencies and low overall effectiveness. Even with attempts to enhance farmers’ welfare through improved seeds and fertilizers, meaningful outcomes are yet to be achieved due to huge amounts of post-harvest losses. Such losses significantly endanger food security, economic stability, and result in unsustainable agricultural practices because more land, water, labor, energy, fertilizer, and other inputs must be used to produce more food. Drying, as a critical post-harvest process involving simultaneous heat and mass transfer, deserves attention. Among alternative green-energy sources, solar energy-based drying garners attention, particularly for small-scale farmers in remote communities. However, the intermittent nature of solar radiation poses challenges. To address this, energy storage solutions like rock-based thermal energy storage offer cost-effective solutions tailored to the needs of farmers. Methodologically, three solar dryers were constructed of metal, wood, and clay brick. Several tests were carried out with and without energy storage material. Notably, it has been demonstrated that soapstone stands out as a promising material due to its affordability and high specific energy capacity. By implementing these greener technologies, Sub-Saharan African countries could mitigate post-harvest losses, enhance food availability, improve nutrition, and promote sustainable resource utilization.

Keywords: energy storage, food security, post-harvest, solar dryer

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Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

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Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: desalting unit, crude oil, neural networks, simulation, recovery, separation

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Authors: Syed Kamran Sami, Saqib Siddiqui

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In recent times, flexible supercapacitors retaining high electrochemical performance and steadiness along with mechanical endurance has developed as a spring of attraction due to the exponential progress and innovations in energy storage devices. To meet the rampant increasing demand of energy storage device with the small form factor, a unique, low cost and high-performance supercapacitor with considerably higher capacitance and mechanical robustness is required to recognize their real-life applications. Here in this report, synthesis route of electrode materials with low rigidity and high charge storage performance is reported using 1D-1D hybrid structure of zinc oxide (ZnO) nanorods, and conductive polymer smeared polyvinylidene fluoride–trifluoroethylene (P(VDF–TrFE)) electrospun nanofibers. The ZnO nanorods were uniformly grown on poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) coated P(VDF-TrFE) nanofibers using hydrothermal growth to manufacture light weight, permeable electrodes for supercapacitor. The PEDOT: PSS coated P(VDF-TrFE) porous web of nanofibers act as framework with high surface area. The incorporation of ZnO nanorods further boost the specific capacitance by 59%. The symmetric device using the fabricated 1D-1D hybrid electrodes reveals fairly high areal capacitance of 1.22mF/cm² at a current density of 0.1 mA/cm² with a power density of more than 1600 W/Kg. Moreover, the fabricated electrodes show exceptional flexibility and high endurance with 90% and 76% specific capacitance retention after 1000 and 5000 cycles respectively signifying the astonishing mechanical durability and long-term stability. All the properties exhibited by the fabricated electrode make it convenient for making flexible energy storage devices with the low form factor.

Keywords: ZnO nanorods, electrospinning, mechanical endurance, flexible supercapacitor

Procedia PDF Downloads 281

Authors: Gurmail Singh

Abstract:

Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

Procedia PDF Downloads 127

Authors: Marwa Djebbi, Hakim Gabtni

Abstract:

Gravity is a continuously advancing method that has become a mature technology for geological studies. Increasingly, it has been used to complement and constrain traditional seismic data and even used as the only tool to get information of the sub-surface. In fact, in some regions the seismic data, if available, are of poor quality and hard to be interpreted. Such is the case for the current study area. The Nefza zone is part of the Tellian fold and thrust belt domain in the north west of Tunisia. It is essentially made of a pile of allochthonous units resulting from a major Neogene tectonic event. Its tectonic and stratigraphic developments have always been subject of controversies. Considering the geological and hydrogeological importance of this area, a detailed interdisciplinary study has been conducted integrating geology, seismic and gravity techniques. The interpretation of Gravity data allowed the delimitation of the dune reservoir and the identification of the regional lineaments contouring the area. It revealed the presence of three gravity lows that correspond to the dune of Zouara and Ouchtata separated along with a positive gravity axis espousing the Ain Allega_Aroub Er Roumane axe. The Bouguer gravity map illustrated the compartmentalization of the Zouara dune into two depressions separated by a NW-SE anomaly trend. This constitution was confirmed by the vertical derivative map which showed the individualization of two depressions with slightly different anomaly values. The horizontal gravity gradient magnitude was performed in order to determine the different geological features present in the studied area. The latest indicated the presence of NE-SW parallel folds according to the major Atlasic direction. Also, NW-SE and EW trends were identified. The maxima tracing confirmed this direction by the presence of NE-SW faults, mainly the Ghardimaou_Cap Serrat accident. The quality of the available seismic sections and the absence of borehole data in the region, except few hydraulic wells that been drilled and showing the heterogeneity of the substratum of the dune, required the process of gravity modeling of this challenging area that necessitates to be modeled for the geometrical characterization of the dune reservoir and determine the different stratigraphic series underneath these deposits. For more detailed and accurate results, the scale of study will be reduced in coming research. A more concise method will be elaborated; the 4D microgravity survey. This approach is considered as an expansion of gravity method and its fourth dimension is time. It will allow a continuous and repeated monitoring of fluid movement in the subsurface according to the micro gal (μgall) scale. The gravity effect is a result of a monthly variation of the dynamic groundwater level which correlates with rainfall during different periods.

Keywords: 3D gravity modeling, dune reservoir, heterogeneous substratum, seismic interpretation

Procedia PDF Downloads 298

Authors: Tariq Ahmad Safapuri, Saghir Ahmad, Farhana Allai

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The present study was conducted to evaluate the effect dehydration techniques (polyhouse and tray drying), different treatment (SHMP, SHMP+ salt, salt + turmeric), different packaging material (HDPE, combination film), and different packaging methods (air, vacuum, CO2 Flush) on quality of dehydrated buffalo meat during ambient temperature storage. The quality measuring parameters included physico-chemical characteristics i.e. pH, rehydration ratio, moisture content and microbiological characteristics viz total plate content. It was found that the treatment of (SHMP, SHMP + salt, salt + turmeric increased the pH. Moisture Content of dehydrated meat samples were found in between 7.20% and 5.54%.the rehydration ratio of salt+ turmeric treated sample was found to be highest and lowest for controlled meat sample. the bacterial count log TPC/g of salt + turmeric and tray dried was lowest i.e. 1.80.During ambient temperature storage ,there was no considerable change in pH of dehydrated sample till 150 days. however the moisture content of samples increased in different packaging system in different manner. The highest moisture rise was found in case of controlled meat sample HDPE/air packed while the lowest increase was reported for SHMP+ Salt treated Packed by vacuum in combination film packed sample. Rehydration ratio was found considerably affected in case of HDPE and air packed sample dehydrated in polyhouse after 150 days of ambient storage. While there was a very little change in the rehydration ratio of meat samples packed in combination film CO2 flush system. The TPC was found under safe limit even after 150 days of storage. The microbial count was found to be lowest for salt+ turmeric treated samples after 150 days of storage.

Keywords: ambient temperature, dehydration technique, rehydration ratio, SHMP (sodium hexa meta phosphate), HDPE (high density polyethelene)

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Authors: Amani Aljahani

Abstract:

The purpose of the study was to identify the physical and chemical properties of pomegranate juices and to evaluate their sensory quality. The samples were collected from the local markets and included four types of pomegranate produced in the western and southern region of the kingdom. The juices were extracted by manual squeezing and by centrifugal force. The juices were analyzed periodically for their content of organic acids, total acidity, glucose and fructose, total sugars, and the anthosianine. A panel of 30 judges evaluated the juices for their color, smell, taste, consistency and general acceptance using a prepared scale for that purpose. Result showed that pomegranate juices were acidic in nature (PH between 3.56–4.27). The major organic acids were citric, tartaric, malic, and oxalic aids total organic acidity was between 596.32–763.49 ng/100 ml and increased over storage time, however; total acidity almost stable over time except for the southern produced. The major monosaccharide's in pomegranate juices were glucose and fructose. Their concentration in the juice varied by storage. On the average glucose concentration was between 6.68–7.71 g/100 ml while fructose concentration was between 6.72–7.98 g/100 ml. total sugars content was 16% on the average and dropped by storage. Anthosianine concertration increased after five hours of storage then dropped and stabilized over time regardless of method of treatment. In addition, sensory evaluation of the juices showed general acceptance of them as of color, flavor, and constercy but the preferred one was with that of the western kind extracted by squeezing.

Keywords: extracting, pomegranate, juice, quality

Procedia PDF Downloads 350