Search results for: teaching-learning based optimization

Authors: Wafaa M. Abd El-Rahim, Magda A. El-Meleigy, Eman Refaat

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This study dealing with optimization the conditions affecting the formation of extracellular lignin- degrading enzymes to achieve maximal decolorization activity of Direct Violet dye by one fungal strain. In this study Aspergillus fumigates fungal strain used for production extracellular ligninolytic enzymes for removing Direct Violet dye under different conditions: culture medium, incubation period, pH and temperatures. The results indicted that the removal efficiency of A. fumigatus was enhanced by addition glucose and peptone to the culture medium. The addition of peptone and glucose was found to increase the decolorization activity of the fungal isolate from 51.38% to 93.74% after 4 days of incubation. The highest production of extracellular lignin degrading enzymes also recorded in Direct Violet dye medium supplemented with peptone and glucose. It was also found the decolorization activity of A. fumigatus was decreased gradually by increasing the incubation period up to 4 days. Also it was found that the fungal strain can grow and produce extracellular ligninolytic enzymes which accompanied by efficient removal of Direct Violet dye in a wide pH range of 4-8. The results also found that the maximal biosynthesis of ligninolytic enzymes which accompanied with maximal removal of Direct Violet dye was obtained at a temperature of 28C. This indicates that the different conditions of culture medium, incubation period, pH and temperatures are effective on dye decolorization on the fungal biomass and played a role in Direct Violet dye removal along with enzymatic activity of A. fumigatus.

Keywords: A. fumigates, extracellular lignin- degrading enzymes, textile dye, dye removing

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Authors: Bin Li, Mei Liu

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Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours.

Keywords: bus transit, arrival time prediction, link-based, path-based

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Authors: Khaled Al-Badani, James Ren, Lisa Li, David Allanson

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Droplet formation is an important process in many engineering systems and manufacturing procedures, which includes welding, biotechnologies, 3D printing, biochemical, biomedical fields and many more. The volume and the characteristics of droplet formation are generally depended on various material properties, microfluidics and fluid mechanics considerations. Hence, a detailed investigation of this process, with the aid of numerical computational tools, are essential for future design optimization and process controls of many engineering systems. This will also improve the understanding of changes in the properties and the structures of materials, during the formation of the droplet, which is important for new material developments to achieve different functions, pending the requirements of the application. For example, the shape of the formed droplet is critical for the function of some final products, such as the welding nugget during Capacitor Discharge Welding process, or PLA 3D printing, etc. Although, most academic journals on droplet formation, focused on issued with material transfer rate, surface tension and residual stresses, the general emphasis on the characteristics of droplet shape has been overlooked. The proposed work for this project will examine theoretical methodologies, experimental techniques, and numerical modelling, using ANSYS FLUENT, to critically analyse and highlight optimization methods regarding the formation of pendant droplet. The project will also compare results from published data with experimental and numerical work, concerning the effects of key material parameters on the droplet shape. These effects include changes in heating/cooling rates, solidification/melting progression and separation/break-up times. From these tests, a set of objectives is prepared, with an intention of improving quality, stability and productivity in modelling metal welding and 3D printing.

Keywords: computer modelling, droplet formation, material distortion, materials forming, welding

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Authors: Zainab Bibi, Afsheen Aman, Shah Ali Ul Qader

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Endo-β-1,4-xylanases [EC 3.2.1.8] are one of the major groups of enzymes that are involved in degradation process of xylan and have several applications in food, textile and paper processing industries. Due to broad utility of endo-β-1,4-xylanase, researchers are focusing to increase the productivity of this hydrolase from various microbial species. Harsh industrial condition, faster reaction rate and efficient hydrolysis of xylan with low risk of contamination are critical requirements of industry that can be fulfilled by synthesizing the enzyme with efficient properties. In the current study, a newly isolated thermophile Geobacillus stearothermophilus KIBGE-IB29 was used in order to attain the maximum production of endo-1,4-β-xylanase. Bacterial culture was isolated from soil, collected around the blast furnace site of a steel processing mill, Karachi. Optimization of various nutritional and physical factors resulted the maximum synthesis of endo-1,4-β-xylanase from a thermophile. High production yield was achieved at 60°C and pH-6.0 after 24 hours of incubation period. Various nitrogen sources viz. peptone, yeast extract and meat extract improved the enzyme synthesis with 0.5%, 0.2% and 0.1% optimum concentrations. Dipotassium hydrogen phosphate (0.25%), potassium dihydrogen phosphate (0.05%), ammonium sulfate (0.05%) and calcium chloride (0.01%) were noticed as valuable salts to improve the production of enzyme. The thermophilic nature of isolate, with its broad pH stability profile and reduced fermentation time indicates its importance for effective xylan saccharification and for large scale production of endo-1,4-β-xylanase.

Keywords: geobacillus, optimization, production, xylanase

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Authors: P. Hooshangitabrizi, I. Contreras, N. Bhuiyan

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This work presents an online scheduling problem which accommodates multiple requests of patients for chemotherapy treatments in a cancer center of a major metropolitan hospital in Canada. To solve the problem, an adaptive flexible approach is proposed which systematically combines two optimization models. The first model is intended to dynamically schedule arriving requests in the form of waiting lists whereas the second model is used to reschedule the already booked patients with the goal of finding better resource allocations when new information becomes available. Both models are created as mixed integer programming formulations. Various controllable and flexible parameters such as deviating the prescribed target dates by a pre-determined threshold, changing the start time of already booked appointments and the maximum number of appointments to move in the schedule are included in the proposed approach to have sufficient degrees of flexibility in handling arrival requests and unexpected changes. Several computational experiments are conducted to evaluate the performance of the proposed approach using historical data provided by the oncology clinic. Our approach achieves outstandingly better results as compared to those of the scheduling system being used in practice. Moreover, several analyses are conducted to evaluate the effect of considering different levels of flexibility on the obtained results and to assess the performance of the proposed approach in dealing with last-minute changes. We strongly believe that the proposed flexible adaptive approach is very well-suited for implementation at the clinic to provide better patient-care services and to utilize available resource more efficiently.

Keywords: chemotherapy scheduling, multi-appointment modeling, optimization of resources, satisfaction of patients, mixed integer programming

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

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Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Zbigniew Czyz, Ksenia Siadkowska, Krzysztof Skiba, Karol Scislowski

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Today’s progress in the rotorcraft is mostly associated with an optimization of aircraft performance achieved by active and passive modifications of main rotor assemblies and a tail propeller. The key task is to improve their performance, improve the hover quality factor for rotors but not change in specific fuel consumption. One of the tasks to improve the helicopter is an active optimization of the main rotor providing for flight stages, i.e., an ascend, flight, a descend. An active interference with the airflow around the rotor blade section can significantly change characteristics of the aerodynamic airfoil. The efficiency of actuator systems modifying aerodynamic coefficients in the current solutions is relatively high and significantly affects the increase in strength. The solution to actively change aerodynamic characteristics assumes a periodic change of geometric features of blades depending on flight stages. Changing geometric parameters of blade warping enables an optimization of main rotor performance depending on helicopter flight stages. Structurally, an adaptation of shape memory alloys does not significantly affect rotor blade fatigue strength, which contributes to reduce costs associated with an adaptation of the system to the existing blades, and gains from a better performance can easily amortize such a modification and improve profitability of such a structure. In order to obtain quantitative and qualitative data to solve this research problem, a number of numerical analyses have been necessary. The main problem is a selection of design parameters of the main rotor and a preliminary optimization of its performance to improve the hover quality factor for rotors. This design concept assumes a three-bladed main rotor with a chord of 0.07 m and radius R = 1 m. The value of rotor speed is a calculated parameter of an optimization function. To specify the initial distribution of geometric warping, a special software has been created that uses a numerical method of a blade element which respects dynamic design features such as fluctuations of a blade in its joints. A number of performance analyses as a function of rotor speed, forward speed, and altitude have been performed. The calculations were carried out for the full model assembly. This approach makes it possible to observe the behavior of components and their mutual interaction resulting from the forces. The key element of each rotor is the shaft, hub and pins holding the joints and blade yokes. These components are exposed to the highest loads. As a result of the analysis, the safety factor was determined at the level of k > 1.5, which gives grounds to obtain certification for the strength of the structure. The construction of the joint rotor has numerous moving elements in its structure. Despite the high safety factor, the places with the highest stresses, where the signs of wear and tear may appear, have been indicated. The numerical analysis carried out showed that the most loaded element is the pin connecting the modular bearing of the blade yoke with the element of the horizontal oscillation joint. The stresses in this element result in a safety factor of k=1.7. The other analysed rotor components have a safety factor of more than 2 and in the case of the shaft, this factor is more than 3. However, it must be remembered that the structure is as strong as the weakest cell is. Designed rotor for unmanned aerial vehicles adapted to work with blades with intelligent materials in its structure meets the requirements for certification testing. Acknowledgement: This work has been financed by the Polish National Centre for Research and Development under the LIDER program, Grant Agreement No. LIDER/45/0177/L-9/17/NCBR/2018.

Keywords: main rotor, rotorcraft aerodynamics, shape memory alloy, materials, unmanned helicopter

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: A. Rong, P. B. Luh, R. Lahdelma

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High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).

Keywords: dynamic programming, multi-site combined heat and power system, relaxed states, transmission-constrained generation unit commitment

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Authors: Adeolu O. Dairo

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Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making.

Keywords: geospatial, geo-analytics, self-organizing map, customer-centric

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Authors: Juan Portoviejo Brito, Daniel Icaza Alvarez, Christian Castro Samaniego

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In this article, we present the modeling, simulations, and energy conversion analysis of the solar-wind system for the Quingeo Heritage Center in Ecuador. A numerical model was constructed based on the 19 equations, it was coded in MATLAB R2017a, and the results were compared with the experimental data of the site. The model is built with the purpose of using it as a computer development for the optimization of resources and designs of hybrid systems in the Parish of Quingeo and its surroundings. The model obtained a fairly similar pattern compared to the data and curves obtained in the field experimentally and detailed in manuscript. It is important to indicate that this analysis has been carried out so that in the near future one or two of these power generation systems can be exploited in a massive way according to the budget assigned by the Parish GAD of Quingeo or other national or international organizations with the purpose of preserving this unique colonial helmet in Ecuador.

Keywords: hybrid system, wind turbine, modeling, simulation, Smart Grid, Quingeo Azuay Ecuador

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Authors: K. Dhanalakshmi, N. Deepak, E. Manikandan, S. Kanagaraj, M. Sulthan Ariff Rahman, P. Chilambarasan C. Abhimanyu, C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

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In this paper using a validated double precision, density-based implicit standard k-ε model, the detailed 2D and 3D numerical studies have been carried out to examine the external flow choking at wing-in-ground (WIG) effect craft. The CFD code is calibrated using the exact solution based on the Sanal flow choking condition for adiabatic flows. We observed that at the identical WIG effect conditions the numerically predicted 2D boundary layer blockage is significantly higher than the 3D case and as a result, the airfoil exhibited an early external flow choking than the corresponding wing, which is corroborated with the exact solution. We concluded that, in lieu of the conventional 2D numerical simulation, it is invariably beneficial to go for a realistic 3D simulation of the wing in ground effect, which is analogous and would have the aspects of a real-time parametric flow. We inferred that under the identical flying conditions the chances of external flow choking at WIG effect is higher for conventional aircraft than an aircraft facilitating a divergent channel effect at the bottom surface of the fuselage as proposed herein. We concluded that the fuselage and wings integrated geometry optimization can improve the overall aerodynamic performance of WIG craft. This study is a pointer to the designers and/or pilots for perceiving the zone of danger a priori due to the anticipated external flow choking at WIG effect craft for safe flying at the close proximity of the terrain and the dynamic surface of the marine.

Keywords: boundary layer blockage, chord dominated ground effect, external flow choking, WIG effect

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Authors: Xiaowei Zhang, Min Xu, Saeid Habibi, Fengjun Yan, Ryan Ahmed

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Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: lithium-ion batteries, genetic algorithm optimization, battery aging test, parameter identification

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: Kumar N., Verma S., Park J., Srivastava V. C.

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Organic carbonates have the potential to be used as fuels and because of this, their production through non-phosgene routes is a thrust area of research. Di-ethyl carbonate (DEC) synthesis from propylene carbonate (PC) in the presence of alcohol is a green route. In this study, the use of reduced graphene oxide (rGO) based metal oxide catalysts [rGO-MO, where M = Ce] with different amounts of graphene oxide (0.2%, 0.5%, 1%, and 2%) has been investigated for the synthesis of DEC by using PC and ethanol as reactants. The GO sheets were synthesized by an electrochemical process and the catalysts were synthesized using an in-situ method. A theoretical study of the thermodynamics of the reaction was done, which revealed that the reaction is mildly endothermic. The theoretical value of optimum temperature was found to be 420 K. The synthesized catalysts were characterized for their morphological, structural and textural properties using field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), N2 adsorption/desorption, thermogravimetric analysis (TGA), and Raman spectroscopy. Optimization studies were carried out to study the effect of different reaction conditions like temperature (140 °C to 180 °C) and catalyst dosage (0.102 g to 0.255 g) on the yield of DEC. Amongst the various synthesized catalysts, 1% rGO-CeO2 gave the maximum yield of DEC.

Keywords: GO, DEC, propylene carbonate, transesterification, thermodynamics

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Authors: Zongjie Wang, Zhizhong Guo

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While the regular optimal power flow model focuses on a single time scan, the optimization of power systems is typically intended for a time duration with respect to a desired objective function. In this paper, a temporal optimal power flow model for a time period is proposed. To reduce the computation burden needed for calculating temporal optimal power flow, a characteristic optimal power flow model is proposed, which employs different characteristic load patterns to represent the objective function and security constraints. A numerical method based on the interior point method is also proposed for solving the characteristic optimal power flow model. Both the temporal optimal power flow model and characteristic optimal power flow model can improve the systems’ desired objective function for the entire time period. Numerical studies are conducted on the IEEE 14 and 118-bus test systems to demonstrate the effectiveness of the proposed characteristic optimal power flow model.

Keywords: optimal power flow, time period, security, economy

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Authors: T. Aboumahboub, R. Brecha, H. B. Shrestha, U. F. Hutfilter, A. Geiges, W. Hare, M. Schaeffer, L. Welder, M. Gidden

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The proposed stringent mitigation targets require an immediate start for a drastic transformation of the whole energy system. The current Australian energy system is mainly centralized and fossil fuel-based in most states with coal and gas-fired plants dominating the total produced electricity over the recent past. On the other hand, the country is characterized by a huge, untapped renewable potential, where wind and solar energy could play a key role in the decarbonization of the Australia’s future energy system. However, integrating high shares of such variable renewable energy sources (VRES) challenges the power system considerably due to their temporal fluctuations and geographical dispersion. This raises the concerns about flexibility gap in the system to ensure the security of supply with increasing shares of such intermittent sources. One main flexibility dimension to facilitate system integration of high shares of VRES is to increase the cross-sectoral integration through coupling of electricity to other energy sectors alongside the decarbonization of the power sector and reinforcement of the transmission grid. This paper applies a multi-sectoral energy system optimization model for Australia. We investigate the cost-optimal configuration of a renewable-based Australian energy system and its transformation pathway in line with the ambitious range of proposed climate change mitigation targets. We particularly analyse the implications of linking the electricity and transport sectors in a prospective, highly renewable Australian energy system.

Keywords: decarbonization, energy system modelling, renewable energy, sector coupling

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Authors: S. Toufigh Bararpour, Davood Karami, Nader Mahinpey

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Dependence of global economics on fossil fuels has led to a large growth in the emission of greenhouse gases (GHGs). Among the various GHGs, carbon dioxide is the main contributor to the greenhouse effect due to its huge emission amount. To mitigate the threatening effect of CO₂, carbon capture and sequestration (CCS) technologies have been studied widely in recent years. For the combustion processes, three main CO₂ capture techniques have been proposed such as post-combustion, pre-combustion and oxyfuel combustion. Post-combustion is the most commonly used CO₂ capture process as it can be readily retrofit into the existing power plants. Multiple advantages have been reported for the post-combustion by solid sorbents such as high CO₂ selectivity, high adsorption capacity, and low required regeneration energy. Chemical adsorption of CO₂ over alkali-metal-based solid sorbents such as K₂CO₃ is a promising method for the selective capture of diluted CO₂ from the huge amount of nitrogen existing in the flue gas. To improve the CO₂ capture performance, K₂CO₃ is supported by a stable and porous material. Al₂O₃ has been employed commonly as the support and enhanced the cyclic CO₂ capture efficiency of K₂CO₃. Different phases of alumina can be obtained by setting the calcination temperature of boehmite at 300, 600 (γ-alumina), 950 (δ-alumina) and 1200 °C (α-alumina). By increasing the calcination temperature, the regeneration capacity of alumina increases, while the surface area reduces. However, sorbents with lower surface areas have lower CO₂ capture capacity as well (except for the sorbents prepared by hydrophilic support materials). To resolve this issue, a highly efficient alumina-aerogel support was synthesized with a BET surface area of over 2000 m²/g and then calcined at a high temperature. The synthesized alumina-aerogel was impregnated on K₂CO₃ based on 50 wt% support/K₂CO₃, which resulted in the preparation of a sorbent with remarkable CO₂ capture performance. The effect of synthesis conditions such as types of alcohols, solvent-to-co-solvent ratios, and aging times was investigated on the performance of the support. The best support was synthesized using methanol as the solvent, after five days of aging time, and at a solvent-to-co-solvent (methanol-to-toluene) ratio (v/v) of 1/5. Response surface methodology was used to investigate the effect of operating parameters such as carbonation temperature and H₂O-to-CO₂ flowrate ratio on the CO₂ capture capacity. The maximum CO₂ capture capacity, at the optimum amounts of operating parameters, was 7.2 mmol CO₂ per gram K₂CO₃. Cyclic behavior of the sorbent was examined over 20 carbonation and regenerations cycles. The alumina-aerogel-supported K₂CO₃ showed a great performance compared to unsupported K₂CO₃ and γ-alumina-supported K₂CO₃. Fundamental performance analyses and long-term thermal and chemical stability test will be performed on the sorbent in the future. The applicability of the sorbent for a bench-scale process will be evaluated, and a corresponding process model will be established. The fundamental material knowledge and respective process development will be delivered to industrial partners for the design of a pilot-scale testing unit, thereby facilitating the industrial application of alumina-aerogel.

Keywords: alumina-aerogel, CO₂ capture, K₂CO₃, optimization

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: A. Zaher, A. Traore, F. Thiéry, T. Talbert, B. Shaer

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In this paper, an intelligent approach is proposed to optimize the orientation of continuous solar tracking systems on cloudy days. Considering the weather case, the direct sunlight is more important than the diffuse radiation in case of clear sky. Thus, the panel is always pointed towards the sun. In case of an overcast sky, the solar beam is close to zero, and the panel is placed horizontally to receive the maximum of diffuse radiation. Under partly covered conditions, the panel must be pointed towards the source that emits the maximum of solar energy and it may be anywhere in the sky dome. Thus, the idea of our approach is to analyze the images, captured by ground-based sky camera system, in order to detect the zone in the sky dome which is considered as the optimal source of energy under cloudy conditions. The proposed approach is implemented using experimental setup developed at PROMES-CNRS laboratory in Perpignan city (France). Under overcast conditions, the results were very satisfactory, and the intelligent approach has provided efficiency gains of up to 9% relative to conventional continuous sun tracking systems.

Keywords: clouds detection, fuzzy inference systems, images processing, sun trackers

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Authors: Adrienn Kiss, Karoly Zauer, Gyorgy Keglevich, Rita Molnarne Bernath

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Ginger (Zingiber officinale) is a perennial plant from Southeast Asia, widely used as a spice, herb, and medicine for many illnesses since its beneficial health effects were observed thousands of years ago. Among the compounds found in ginger, zingerone [4-hydroxy-3- methoxyphenyl-2-butanone] deserves special attention: it has an anti-inflammatory and antispasmodic effect, it can be used in case of diarrheal disease, helps to prevent the formation of blood clots, has antimicrobial properties, and can also play a role in preventing the Alzheimer's disease. Ferulic acid [(E)-3-(4-hydroxy-3-methoxyphenyl)-prop-2-enoic acid] is another cinnamic acid derivative in ginger, which has promising properties. Like many phenolic compounds, ferulic acid is also an antioxidant. Based on the results of animal experiments, it is assumed to have a direct antitumoral effect in lung and liver cancer. It also deactivates free radicals that can damage the cell membrane and the DNA and helps to protect the skin against UV radiation. The aim of this work was to synthesize these two compounds by new methods. A few of the reactions were based on the hydrogenation of dehydrozingerone [4-(4-Hydroxy-3-methoxyphenyl)-3-buten-2-one] to zingerone. Dehydrozingerone can be synthesized by a relatively simple method from acetone and vanillin with good yield (80%, melting point: 41 °C). Hydrogenation can be carried out chemically, for example by the reaction of zinc and acetic acid, or Grignard magnesium and ethyl alcohol. Another way to complete the reduction is the electrochemical pathway. The electrolysis of dehydrozingerone without diaphragm in aqueous media was attempted to produce ferulic acid in the presence of sodium carbonate and potassium iodide using platinum electrodes. The electrolysis of dehydrozingerone in the presence of potassium carbonate and acetic acid to prepare zingerone was carried out similarly. Ferulic acid was expected to be converted to dihydroferulic acid [3-(4-Hydroxy-3-methoxyphenyl)propanoic acid] in potassium hydroxide solution using iron electrodes, separating the anode and cathode space with a Soxhlet paper sheath impregnated with saturated magnesium chloride solution. For this reaction, ferulic acid was synthesized from vanillin and malonic acid in the presence of pyridine and piperidine (yield: 88.7%, melting point: 173°C). Unfortunately, in many cases, the expected transformations did not happen or took place in low conversions, although gas evolution occurred. Thus, a deeper understanding of these experiments and optimization are needed. Since both compounds are found in different plants, they can also be obtained by alkaline extraction or steam distillation from distinct plant parts (ferulic acid from ground bamboo shoots, zingerone from grated ginger root). The products of these reactions are rich in several other organic compounds as well; therefore, their separation must be solved to get the desired pure material. The products of the reactions described above were characterized by infrared spectral data and melting points. The use of these two simple methods may be informative for the formation of the products. In the future, we would like to study the ferulic acid and zingerone content of other plants and extract them efficiently. The optimization of electrochemical reactions and the use of other test methods are also among our plans.

Keywords: ferulic acid, ginger, synthesis, zingerone

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Authors: Apurva Patil, Ashay Aswale, Akshay Kulkarni, Shubham Bharadiya

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The potential benefit of gravity compensation of linkages in mechanisms using springs to reduce actuator requirements is well recognized, but practical applications have been elusive. Although existing methods provide exact spring balance, they require additional masses or auxiliary links, or all the springs used originate from the ground, which makes the resulting device bulky and space-inefficient. This paper uses a method of static balancing of mechanisms with conservative loads such as gravity and spring loads using non-zero-free-length springs with child–parent connections and no auxiliary links. Application of this method to the developed arm of a humanoid robot is presented here. Spring balancing is particularly important in this case because the serial chain of linkages has to work against gravity.This work involves approximate spring balancing of the open-loop chain of linkages using minimization of potential energy variance. It uses the approach of flattening the potential energy distribution over the workspace and fuses it with numerical optimization. The results show the considerable reduction in actuator torque requirement with practical spring design and arrangement. Reduced actuator torque facilitates the use of lower end actuators which are generally smaller in weight and volume thereby lowering the space requirements and the total weight of the arm. This is particularly important for humanoid robots where the parent actuator has to handle the weight of the subsequent actuators as well. Actuators with lower actuation requirements are more energy efficient, thereby reduce the energy consumption of the mechanism. Lower end actuators are lower in cost and facilitate the development of low-cost devices. Although the method provides only an approximate balancing, it is versatile, flexible in choosing appropriate control variables that are relevant to the design problem and easy to implement. The true potential of this technique lies in the fact that it uses a very simple optimization to find the spring constant, free-length of the spring and the optimal attachment points subject to the optimization constraints. Also, it uses physically realizable non-zero-free-length springs directly, thereby reducing the complexity involved in simulating zero-free-length springs from non-zero-free-length springs. This method allows springs to be attached to the preceding parent link, which makes the implementation of spring balancing practical. Because auxiliary linkages can be avoided, the resultant arm of the humanoid robot is compact. The cost benefits and reduced complexity can be significant advantages in the development of this arm of the humanoid robot.

Keywords: actuator torque, child-parent connections, spring balancing, the arm of a humanoid robot

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Authors: Aleksandar Hatzivelkos

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The term ”compromise” is mostly used casually within the social choice theory. It is usually used as a mere result of the social choice function, and this omits its deeper meaning and ramifications. This paper is based on a mathematical model for the description of a compromise as a version of the Sorites paradox. It introduces a formal definition of d-measure of divergence from a compromise and models a notion of compromise that is often used only colloquially. Such a model for vagueness phenomenon, which lies at the core of the notion of compromise enables the introduction of new mathematical structures. In order to maximize compromise, different methods can be used. In this paper, we explore properties of a social welfare function TdM (from Total d-Measure), which is defined as a function which minimizes the total sum of d-measures of divergence over all possible linear orderings. We prove that TdM satisfy strict Pareto principle and behaves well asymptotically. Furthermore, we show that for certain domain restrictions, TdM satisfy positive responsiveness and IIIA (intense independence of irrelevant alternatives) thus being equivalent to Borda count on such domain restriction. This result gives new opportunities in social choice, especially when there is an emphasis on compromise in the decision-making process.

Keywords: borda count, compromise, measure of divergence, minimization

Procedia PDF Downloads 133

Authors: Lan Luo

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In China’s construction industry, it is a normal practice to separately subcontract the decoration engineering part from construction engineering, and Building Information Modeling (BIM) is also done separately. Application of BIM in decoration engineering should be integrated with other disciplines, but Chinese current practice makes this very difficult and complicated. Currently, there are three barriers in the auditing of BIM application in decoration engineering in China: heavy workload; scarcity of qualified professionals; and lack of literature concerning audit contents, standards, and methods. Therefore, it is significant to perform research on what (contents) should be evaluated, in which phase, and by whom (professional qualifications) in BIM application in decoration construction so that the application of BIM can be promoted in a better manner. Based on this consideration, four principles of BIM auditing are proposed: Comprehensiveness of information, accuracy of data, aesthetic attractiveness of appearance, and scheme optimization. In the model audit, three methods should be used: Collision, observation, and contrast. In addition, BIM auditing at six stages is discussed and a checklist for work items and results to be submitted is proposed. This checklist can be used for reference by decoration project participants.

Keywords: audit, evaluation, dimensions, methods, standards, BIM application in decoration engineering projects

Procedia PDF Downloads 343

Authors: Meiyuan Zheng, Jingwu He, Yuexi Xiong

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A universal multi-function leaf spring main landing gear was designed for light aircraft. The main landing gear combined with the leaf spring, skidding, and wheels enables it to have a good takeoff and landing performance on various grounds such as the hard, snow, grass and sand grounds. Firstly, the characteristics of different landing sites were studied in this paper in order to analyze the load of the main landing gear on different types of grounds. Based on this analysis, the structural design optimization along with the strength and stiffness characteristics of the main landing gear has been done, which enables it to have good takeoff and landing performance on different types of grounds given the relevant regulations and standards. Additionally, the impact of the skidding on the aircraft during the flight was also taken into consideration. Finally, a universal multi-function leaf spring type of the main landing gear suitable for light aircraft has been developed.

Keywords: landing gear, multi-function, leaf spring, skidding

Procedia PDF Downloads 268

Authors: Fatima Zahra Falah, Touria El. Ghailassi, Samia Yousfi, Ahmed Moussaif, Hasna Hamdane, Mouna Latifa Bouamrani

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The management and treatment of radioactive wastewater pose significant challenges to environmental safety and public health. This study presents an innovative approach to optimizing radioactive wastewater treatment using a novel biosorbent: chitosan-argan nutshell beads. By employing Response Surface Methodology (RSM), we aimed to determine the optimal conditions for maximum removal efficiency of radioactive contaminants. Chitosan, a biodegradable and non-toxic biopolymer, was combined with argan nutshell powder to create composite beads. The argan nutshell, a waste product from argan oil production, provides additional adsorption sites and mechanical stability to the biosorbent. The beads were characterized using Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD) to confirm their structure and composition. A three-factor, three-level Box-Behnken design was utilized to investigate the effects of pH (3-9), contact time (30-150 minutes), and adsorbent dosage (0.5-2.5 g/L) on the removal efficiency of radioactive isotopes, primarily focusing on cesium-137. Batch adsorption experiments were conducted using synthetic radioactive wastewater with known concentrations of these isotopes. The RSM analysis revealed that all three factors significantly influenced the adsorption process. A quadratic model was developed to describe the relationship between the factors and the removal efficiency. The model's adequacy was confirmed through analysis of variance (ANOVA) and various diagnostic plots. Optimal conditions for maximum removal efficiency were pH 6.8, a contact time of 120 minutes, and an adsorbent dosage of 0.8 g/L. Under these conditions, the experimental removal efficiency for cesium-137 was 94.7%, closely matching the model's predictions. Adsorption isotherms and kinetics were also investigated to elucidate the mechanism of the process. The Langmuir isotherm and pseudo-second-order kinetic model best described the adsorption behavior, indicating a monolayer adsorption process on a homogeneous surface. This study demonstrates the potential of chitosan-argan nutshell beads as an effective and sustainable biosorbent for radioactive wastewater treatment. The use of RSM allowed for the efficient optimization of the process parameters, potentially reducing the time and resources required for large-scale implementation. Future work will focus on testing the biosorbent's performance with real radioactive wastewater samples and investigating its regeneration and reusability for long-term applications.

Keywords: adsorption, argan nutshell, beads, chitosan, mechanism, optimization, radioactive wastewater, response surface methodology

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Authors: M. Almutairi, S. Hadjiloucas

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The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: harmonics, passive filter, power factor, power quality

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Authors: Michelangelo Sofo, Giuseppe Labianca

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In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

Procedia PDF Downloads 23