Search results for: operational error

Authors: Ankur Sachan

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The ability to make quick yet intelligent and value-added decisions to develop new fields has always been of great significance. In situations where the capital expenses and subsurface risk are high, carefully analyzing the inherent uncertainties in the reservoir and how they impact the predicted hydrocarbon accumulation and production becomes a daunting task. The problem is compounded in offshore environments, especially in the presence of heavy oils and disconnected sands where the margin for error is small. Uncertainty refers to the degree to which the data set may be in error or stray from the predicted values. To understand and quantify the uncertainties in reservoir model is important when estimating the reserves. Uncertainty parameters can be geophysical, geological, petrophysical etc. Identification of these parameters is necessary to carry out the uncertainty analysis. With so many uncertainties working at different scales, it becomes essential to have a consistent and efficient way of incorporating them into our analysis. Ranking the uncertainties based on their impact on reserves helps to prioritize/ guide future data gathering and uncertainty reduction efforts. Assigning probabilistic ranges to key uncertainties also enables the computation of probabilistic reserves. With this in mind, this paper, with the help the uncertainty and optimization process in petrel RE shows how the most influential uncertainties can be determined efficiently and how much impact so they have on the reservoir model thus helping in determining a cost effective and accurate model of the reservoir.

Keywords: uncertainty, reservoir model, parameters, optimization analysis

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Authors: Othman Maklouf, Abdunnaser Tresh

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GPS and inertial navigation system (INS) have complementary qualities that make them ideal use for sensor fusion. The limitations of GPS include occasional high noise content, outages when satellite signals are blocked, interference and low bandwidth. The strengths of GPS include its long-term stability and its capacity to function as a stand-alone navigation system. In contrast, INS is not subject to interference or outages, have high bandwidth and good short-term noise characteristics, but have long-term drift errors and require external information for initialization. A combined system of GPS and INS subsystems can exhibit the robustness, higher bandwidth and better noise characteristics of the inertial system with the long-term stability of GPS. The most common estimation algorithm used in integrated INS/GPS is the Kalman Filter (KF). KF is able to take advantages of these characteristics to provide a common integrated navigation implementation with performance superior to that of either subsystem (GPS or INS). This paper presents a simplified KF algorithm for land vehicle navigation application. In this integration scheme, the GPS derived positions and velocities are used as the update measurements for the INS derived PVA. The KF error state vector in this case includes the navigation parameters as well as the accelerometer and gyroscope error states.

Keywords: GPS, INS, Kalman filter, inertial navigation system

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Authors: Muhammad A. Alsubaie

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An iterative learning control framework designed in state feedback structure suffers a lack in investigating load disturbance considerations. The presented work discusses the controller previously designed, highlights the disturbance problem, finds new conditions using singular value principle to assure safe operation conditions with error convergence and reference tracking under the influence of load disturbance. It is known that periodic disturbances can be represented by a delay model in a positive feedback loop acting on the system input. This model can be manipulated by isolating the delay model and finding a controller for the overall system around the delay model to remedy the periodic disturbances using the small signal theorem. The overall system is the base for control design and load disturbance investigation. The major finding of this work is the load disturbance condition found which clearly sets safe operation condition under the influence of load disturbances such that the error tends to nearly zero as the system keeps operating trial after trial.

Keywords: iterative learning control, singular values, state feedback, load disturbance

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Authors: Vibha Devi, Shabina Khanam

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Hemp (Cannabis sativa) possesses a rich content of ω-6 linoleic and ω-3 linolenic essential fatty acid in the ratio of 3:1, which is a rare and most desired ratio that enhances the quality of hemp oil. These components are beneficial for the development of cell and body growth, strengthen the immune system, possess anti-inflammatory action, lowering the risk of heart problem owing to its anti-clotting property and a remedy for arthritis and various disorders. The present study employs supercritical fluid extraction (SFE) approach on hemp seed at various conditions of parameters; temperature (40 - 80) °C, pressure (200 - 350) bar, flow rate (5 - 15) g/min, particle size (0.430 - 1.015) mm and amount of co-solvent (0 - 10) % of solvent flow rate through central composite design (CCD). CCD suggested 32 sets of experiments, which was carried out. As SFE process includes large number of variables, the present study recommends the application of resampling techniques for cross-validation of the obtained data. Cross-validation refits the model on each data to achieve the information regarding the error, variability, deviation etc. Bootstrap and jackknife are the most popular resampling techniques, which create a large number of data through resampling from the original dataset and analyze these data to check the validity of the obtained data. Jackknife resampling is based on the eliminating one observation from the original sample of size N without replacement. For jackknife resampling, the sample size is 31 (eliminating one observation), which is repeated by 32 times. Bootstrap is the frequently used statistical approach for estimating the sampling distribution of an estimator by resampling with replacement from the original sample. For bootstrap resampling, the sample size is 32, which was repeated by 100 times. Estimands for these resampling techniques are considered as mean, standard deviation, variation coefficient and standard error of the mean. For ω-6 linoleic acid concentration, mean value was approx. 58.5 for both resampling methods, which is the average (central value) of the sample mean of all data points. Similarly, for ω-3 linoleic acid concentration, mean was observed as 22.5 through both resampling. Variance exhibits the spread out of the data from its mean. Greater value of variance exhibits the large range of output data, which is 18 for ω-6 linoleic acid (ranging from 48.85 to 63.66 %) and 6 for ω-3 linoleic acid (ranging from 16.71 to 26.2 %). Further, low value of standard deviation (approx. 1 %), low standard error of the mean (< 0.8) and low variance coefficient (< 0.2) reflect the accuracy of the sample for prediction. All the estimator value of variance coefficients, standard deviation and standard error of the mean are found within the 95 % of confidence interval.

Keywords: resampling, supercritical fluid extraction, hemp oil, cross-validation

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Authors: Mandar Ghodekar, Sharad Jadhav, Sangram Jadhav

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Speed control of rotor during startup and under varying load conditions is one of the most difficult tasks of gas turbine operation. In this paper, power plant gas turbine (GE9001E) is considered for this purpose and fuzzy and fuzzy-PI rotor speed controllers are designed. The goal of the presented controllers is to keep the turbine rotor speed within predefined limits during startup condition as well as during operating condition. The fuzzy controller and fuzzy-PI controller are designed using Takagi-Sugeno method and Mamdani method, respectively. In applying the fuzzy-PI control to a gas-turbine plant, the tuning parameters (Kp and Ki) are modified online by fuzzy logic approach. Error and rate of change of error are inputs and change in fuel flow is output for both the controllers. Hence, rotor speed of gas turbine is controlled by modifying the fuel ƒflow. The identified linear ARX model of gas turbine is considered while designing the controllers. For simulations, demand power is taken as disturbance input. It is assumed that inlet guide vane (IGV) position is fixed. In addition, the constraint on the fuel flow is taken into account. The performance of the presented controllers is compared with each other as well as with H∞ robust and MPC controllers for the same operating conditions in simulations.

Keywords: gas turbine, fuzzy controller, fuzzy PI controller, power plant

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Authors: Kwan Hee Han

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External environment of enterprise is rapidly changing majorly by global competition, cost reduction pressures, and new technology. In these situations, production scheduling function plays a critical role to meet customer requirements and to attain the goal of operational efficiency. It deals with short-term decision making in the production process of the whole supply chain. The major task of production scheduling is to seek a balance between customer orders and limited resources. In manufacturing companies, this task is so difficult because it should efficiently utilize resource capacity under the careful consideration of many interacting constraints. At present, many computerized software solutions have been utilized in many enterprises to generate a realistic production schedule to overcome the complexity of schedule generation. However, most production scheduling systems do not provide sufficient information about the validity of the generated schedule except limited statistics. Process mining only recently emerged as a sub-discipline of both data mining and business process management. Process mining techniques enable the useful analysis of a wide variety of processes such as process discovery, conformance checking, and bottleneck analysis. In this study, the performance of generated production schedule is evaluated by mining event log data of production scheduling software system by using the process mining techniques since every software system generates event logs for the further use such as security investigation, auditing and error bugging. An application of process mining approach is proposed for the validation of the goodness of production schedule generated by scheduling software systems in this study. By using process mining techniques, major evaluation criteria such as utilization of workstation, existence of bottleneck workstations, critical process route patterns, and work load balance of each machine over time are measured, and finally, the goodness of production schedule is evaluated. By using the proposed process mining approach for evaluating the performance of generated production schedule, the quality of production schedule of manufacturing enterprises can be improved.

Keywords: data mining, event log, process mining, production scheduling

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Authors: Jongpil La, Jieun Choi

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Optical frequency modulation technology for FMCW LiDAR based on Optical Phase Locked Loop(OPLL) configuration is addressed in this paper. The spectral linewidth measurement method of the linear frequency-modulated laser is also described. The single-frequency laser with narrow spectral linewidth is generated using an external cavity diode laser and the excitation frequency of the laser is adjusted by controlling the injection current of the laser. If the injection current of the laser is increased, the lasing frequency is decreased because of the slight increase in the refractive index of the laser gain chip. Dynamic optical frequency change rate is measured by using a Mach-Zehnder interferometer and compared with a proper reference signal. The phase difference between the reference signal and the measured signal using the Mach-Zehnder interferometer is obtained by mixing those two signals. The phase error is used to detect the frequency deviation error from the target value, which is then fed back to the driving current of the laser to compensate for it. The frequency sweep error from the ideal linear frequency waveform will degrade the spectral linewidth of the target spectrum and will degrade the maximum range performance of FMCW LiDAR. Therefore, the spectral linewidth measurement of frequency modulated laser is very important to evaluate the performance of the LiDAR system. However, it is impossible to apply the conventional self-homodyne or self-heterodyne method with a long delay line to evaluate the spectral linewidth of the frequency-modulated laser because the beat frequency generated by the long delay line is too high to measure with a high bandwidth frequency modulated laser. In this article, the spectral linewidth of the frequency-modulated laser is measured by using the newly proposed self-heterodyne method with a short delay line. The theoretical derivation for the proposed linewidth measurement method is provided in this article. The laser's spectral modulation bandwidth and linewidth are measured as 2.91GHz and 287kHz, respectively. LiDAR.

Keywords: FMCW, LiDAR, spectral linewidth, self-heterodyne

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Authors: Ambalika Ekka

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In today’s world, the growing demand for urban built forms has resulted in the production and consumption of building materials i.e. embodied energy in building construction, leading to pollution and greenhouse gas (GHG) emissions. Therefore, new buildings will offer a unique opportunity to implement more energy efficient building without compromising on building performance of the building. Embodied energy of building materials forms major contribution to embodied energy in buildings. The paper results in an approach towards designing an energy efficient apartment building through embodied energy assessment. This paper discusses the trend of residential development in Rourkela, which includes three case studies of the contemporary houses, followed by architectural elements, number of storeys, predominant material use and plot sizes using primary data. It results in identification of predominant material used and other characteristics in urban area. Further, the embodied energy coefficients of various dominant building materials and alternative materials manufactured in Indian Industry is taken in consideration from secondary source i.e. literature study. The paper analyses the embodied energy by estimating materials and operational energy of proposed building followed by altering the specifications of the materials based on the building components i.e. walls, flooring, windows, insulation and roof through res build India software and comparison of different options is assessed with consideration of sustainable parameters. This paper results that autoclaved aerated concrete block only reaches the energy performance Index benchmark i.e. 69.35 kWh/m² yr i.e. by saving 4% of operational energy and as embodied energy has no particular index, out of all materials it has the highest EE 23206202.43  MJ.

Keywords: energy efficient, embodied energy, EPI, building materials

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Authors: Ahmed Belgasem, Walid Ben Hussin, Emad Krekshi, Jamal Hashad

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Waxy crude oil, characterized by its high paraffin wax content, poses significant challenges in the oil & gas industry due to its increased viscosity and semi-solid state at reduced temperatures. The wax formation process, which includes precipitation, crystallization, and deposition, becomes problematic when crude oil temperatures fall below the wax appearance temperature (WAT) or cloud point. Addressing these issues, this paper introduces a technical solution designed to mitigate the wax appearance and enhance the oil production process in Abu-Attifil Field via a 30-inch crude oil pipeline. A comprehensive flow assurance study validates the feasibility and performance of this solution across various production rates, temperatures, and operational scenarios. The study's findings indicate that maintaining the crude oil's temperature above a minimum threshold of 63°C is achievable through the strategic placement of two heating stations along the pipeline route. This approach effectively prevents wax deposition, gelling, and subsequent mobility complications, thereby bolstering the overall efficiency, reliability, safety, and economic viability of the production process. Moreover, this solution significantly curtails the environmental repercussions traditionally associated with wax deposition, which can accumulate up to 7,500kg. The research methodology involves a comprehensive flow assurance study to validate the feasibility and performance of the proposed solution. The study considers various production rates, temperatures, and operational scenarios. It includes crude oil analysis to determine the wax appearance temperature (WAT), as well as the evaluation and comparison of operating options for the heating stations. The study's findings indicate that the proposed solution effectively prevents wax deposition, gelling, and subsequent mobility complications. By maintaining the crude oil's temperature above the specified threshold, the solution improves the overall efficiency, reliability, safety, and economic viability of the oil production process. Additionally, the solution contributes to reducing environmental repercussions associated with wax deposition. The research conclusion presents a technical solution that optimizes oil production in the Abu-Attifil Field by addressing wax formation problems through the strategic placement of two heating stations. The solution effectively prevents wax deposition, improves overall operational efficiency, and contributes to environmental sustainability. Further research is suggested for field data validation and cost-benefit analysis exploration.

Keywords: oil production, wax depositions, solar cells, heating stations

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Authors: Soebowo Musa

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Mining industries are known to be very volatile due to their sensitive nature toward changes in the environment, particularly coal mining. Heavy equipment distributors and coal mining contractors are among heavily affected by such volatility. They are facing more uncertainty on the sustainability of the coal mining industry. Strategic alliances and organizational capabilities such as innovation capability have long been seen as ways to stay competitive with a focus more on the strategic alliances partner-to-partner in serving their customers. In today’s rapid change in the environment, a shift in consumer behaviors, and the human-centric business approach, this study looks at the strategic alliance partner-to-customer relationship in both the industrial organization and resource-based theories. This study was conducted based on 250 respondents from the strategic alliances partner-to-customer between heavy equipment distributors and coal mining contractors in Indonesia. This study finds strategic alliances have the highest association toward cost reduction, a proxy of operational efficiency followed by its association toward innovation capability. Further, strategic alliances and innovation capability have a positive relationship with customer loyalty, while innovation capability and customer loyalty have no significant relationships toward the firm’s competitive advantage. This study also indicates that cost reduction is not a condition to develop customer loyalty in the strategic alliance partner-to-customer relationship. It confirms strategic alliances are a strategy that creates a firm’s operational efficiency, innovation capability that develops customer loyalty, and competitive advantage.

Keywords: strategic alliance, innovation capability, cost reduction, customer loyalty, competitive advantage

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Li-Hsueh Chen

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Efficient operation is more and more important for managers of hotels. Unlike the manufacturing industry, hotels cannot store their products. In addition, many hotels provide room service, and food and beverage service simultaneously. When efficiencies of hotels are evaluated, the internal structure should be considered. Hence, based on the operational characteristics of hotels, this study proposes a DEA model to simultaneously assess the efficiencies among the room production division, food and beverage production division, room service division and food and beverage service division. However, not only the enhancement of efficiency but also the adjustment of scale can improve the performance. In terms of the adjustment of scale, scale elasticity or returns to scale can help to managers to make decisions concerning expansion or contraction. In order to construct a reasonable approach to measure the efficiencies and scale elasticities of hotels, this study builds an alternative variable-returns-to-scale-based two-stage network DEA model with the combination of parallel and series structures to explore the scale elasticities of the whole system, room production division, food and beverage production division, room service division and food and beverage service division based on the data of international tourist hotel industry in Taiwan. The results may provide valuable information on operational performance and scale for managers and decision makers.

Keywords: efficiency, scale elasticity, network data envelopment analysis, international tourist hotel

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Authors: Chien-Lin Wang, Cha-Ling Ko, Tainsong Chen

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Pulse Blood pressure (BP) is one of the vital signs, and is an index that helps determining the stability of life. In this respect, some spinal cord injury patients need to take the tilt table test. While doing the test, the posture changes abruptly, and may cause a patient’s BP to change abnormally. This may cause patients to feel discomfort, and even feel as though their life is threatened. Therefore, if a continuous non-invasive BP assessment system were built, it could help to alert health care professionals in the process of rehabilitation when the BP value is out of range. In our research, BP assessed by the pulse transit time technique was developed. In the system, we use a self-made photoplethysmograph (PPG) sensor and filter circuit to detect two PPG signals and to calculate the time difference. The BP can immediately be assessed by the trend line. According to the results of this study, the relationship between the systolic BP and PTT has a highly negative linear correlation (R2=0.8). Further, we used the trend line to assess the value of the BP and compared it to a commercial sphygmomanometer (Omron MX3); the error rate of the system was found to be in the range of ±10%, which is within the permissible error range of a commercial sphygmomanometer. The continue blood pressure measurement from pulse transit time technique may have potential to become a convenience method for clinical rehabilitation.

Keywords: continous blood pressure measurement, PPG, time transit time, transit velocity

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Authors: Eugene Mashapa

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The age of information has witnessed countless unprecedented technological developments, which signal the articulation of succinct technological capabilities that can match these cutting-edge technological trends. These changes have impacted patterns in the production, distribution, and consumption of media content, a space that the Film and Publication Board (FPB) is concerned with. Consequently, the FPB is keen to understand the nature and impact of these technological changes on media content regulation. This exploratory study sought to investigate how content regulators in high and middle-income economies have adapted to the changes in this space, seeking insights into innovations, technological and operational, that facilitate continued relevance during this fast-changing environment. The study is aimed at developing recommendations that could assist and inform the organisation in regulating media content as it evolves. Thus, the overall research strategy in this analysis is applied research, and the analytical model adopted is a mixed research design guided by both qualitative and quantitative research instruments. It was revealed in the study that the FPB was significantly impacted by the unprecedented technological advancements in the media regulation space. Additionally, there exists a need for the FPB to understand the current and future penetrations of 4IR technology in the industry and its impact on media governance and policy implementation. This will range from reskilling officials to align with the technological skills to developing technological innovations as well as adopting co-regulatory or self-regulatory arrangements together with content distributors, where more content is distributed in higher volumes and with increased frequency. Importantly, initiating an interactive learning process for both FPB employees and the general public can assist the regulator and improve FPB’s operational efficiency and effectiveness.

Keywords: media, regulation, technology, film and publications board

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Authors: Kuldeep Rawat, Michael O'Shea, Maureen McGough

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The paper will discuss a National Institute of Justice (NIJ) funded project to provide cost-effective aviation technologies and research to support counter-poaching operations related to endangered, protected, and/or regulated wildlife. The goal of this project is to provide cost-effective aviation technology and research support to Kenya Wildlife Service (KWS) in their counter-poaching efforts. In pursuit of this goal, Elizabeth City State University (ECSU) is assisting the National Institute of Justice (NIJ) in enhancing the Kenya Wildlife Service’s aviation technology and related capacity to meet its counter-poaching mission. Poaching, at its core, is systemic as poachers go to the most extreme lengths to kill high target species such as elephant and rhino. These high target wildlife species live in underdeveloped or impoverished nations, where poachers find fewer barriers to their operations. In Kenya, with fifty-nine (59) parks and reserves, spread over an area of 225,830 square miles (584,897 square kilometers) adequate surveillance on the ground is next to impossible. Cost-effective aviation surveillance technologies, based on a comprehensive needs assessment and operational evaluation, are needed to curb poaching and effectively prevent wildlife trafficking. As one of the premier law enforcement Air Wings in East Africa, KWS plays a crucial role in Kenya, not only in counter-poaching and wildlife conservation efforts, but in aerial surveillance, counterterrorism and national security efforts as well. While the Air Wing has done, a remarkable job conducting aerial patrols with limited resources, additional aircraft and upgraded technology should significantly advance the Air Wing’s ability to achieve its wildlife protection mission. The project includes: (i) Needs Assessment of the KWS Air Wing, to include the identification of resources, current and prospective capacity, operational challenges and priority goals for expansion, (ii) Acquisition of Low-Cost Aviation Technology to meet priority needs, and (iii) Operational Evaluation of technology performance, with a focus on implementation and effectiveness. The Needs Assessment reflects the priorities identified through two site visits to the KWS Air Wing in Nairobi, Kenya, as well as field visits to multiple national parks receiving aerial support and interviewing/surveying KWS Air wing pilots and leadership. Needs Assessment identified some immediate technology needs that includes, GPS with upgrades, including weather application, Night flying capabilities, to include runway lights and night vision technology, Cameras and surveillance equipment, Flight tracking system and/or Emergency Position Indicating Radio Beacon, Lightweight ballistic-resistant body armor, and medical equipment, to include a customized stretcher and standard medical evacuation equipment. Results of this assessment, along with significant input from the KWS Air Wing, will guide the second phase of this project: technology acquisition. Acquired technology will then be evaluated in the field, with a focus on implementation and effectiveness. Results will ultimately be translated for any rural or tribal law enforcement agencies with comparable aerial surveillance missions and operational environments, and jurisdictional challenges, seeking to implement low-cost aviation technology. Results from Needs Assessment phase, including survey results and our ongoing technology acquisition and baseline operational evaluation will be discussed in the paper.

Keywords: aerial surveillance mission, aviation technology, counter-poaching, wildlife protection

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Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

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This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

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Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

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Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees

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The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.

Keywords: solar energy, solar radiation, ERA-5, potential energy

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Authors: Piyush V. Surjagade, Jiamei Deng, Paul Doney, S. R. Shimjith, A. John Arul

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In sophisticated and complex systems such as nuclear power plants, maintaining the system's stability in the presence of uncertainties and disturbances and obtaining a fast dynamic response are the most challenging problems. Thus, to ensure the satisfactory and safe operation of nuclear power plants, this work proposes a new fast, robust optimal switching control strategy for pressurized water reactor-type nuclear power plants. The proposed control strategy guarantees a substantial degree of robustness, fast dynamic response over the entire operational envelope, and optimal performance during the nominal operation of the plant. To improve the robustness, obtain a fast dynamic response, and make the system optimal, a bank of controllers is designed. Various controllers, like a baseline proportional-integral-derivative controller, an optimal linear quadratic Gaussian controller, and a robust adaptive L1 controller, are designed to perform distinct tasks in a specific situation. At any instant of time, the most suitable controller from the bank of controllers is selected using the switching logic unit that designates the controller by monitoring the health of the nuclear power plant or transients. The proposed switching control strategy optimizes the overall performance and increases operational safety and efficiency. Simulation studies have been performed considering various uncertainties and disturbances that demonstrate the applicability and effectiveness of the proposed switching control strategy over some conventional control techniques.

Keywords: switching control, robust control, optimal control, nuclear power control

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Authors: Mario Spremic

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Despite the financial crisis and ongoing need for cost cutting, companies all around the world heavily invest in information systems (IS) and underlying information technology (IT). Information systems (IS) play very important role in modern business organizations supporting its organizational efficiency or, under certain circumstances, fostering business model innovation and change. IS can influence organization competitiveness in two ways: supporting operational efficiency (IS as a main infrastructure for the current business), or differentiating business through business model innovation and business process change. In either way, IS becomes very important to the business and needs to be aligned with strategic objectives in order to justify massive investments. A number of studies showed that investments in IS and underlying IT resulted in added business value if they are truly connected with strategic business objectives. In that sense proliferation of governance of enterprise IT helps companies manage, or rather, governs IS as a primary business function with executive management involved in making a decision about IS and IT. The quality of IT governance is rising with the large number of decisions about IS made by executive management, not IT departments. The more executive management is engaged in making a decision about IS and IT, the IT governance is of better quality. In this paper, the practice of governing the enterprise IT will be investigated on a sample of the largest 100 Croatian companies. Research questions posed here will reveal if there are some formal IT governance mechanisms, are there any differences in perceived role of IS and IT between CIOs (Chief Information Officers) and CEOs (Chief Executive Officers) of the sampled companies and what are the mechanisms to govern massive investment in enterprise IT.

Keywords: IT governance, governance of enterprise IT, information system auditing, operational efficiency

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Authors: Jared Abuga, Wesley Too

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Medical error is the third leading cause of death in US, with approximately 98,000 deaths occurring every year as a result of medical errors. The world financial burden of medication errors is roughly USD 42 billion. Medication errors may lead to at least one death daily and injure roughly 1.3 million people every year. Medical error reporting is essential in creating a culture of accountability in our healthcare system. Studies have shown that attitudes and practice of healthcare workers in reporting medical errors showed that the major factors in under-reporting of errors included work stress and fear of medico-legal consequences due to the disclosure of error. Further, the majority believed that increase in reporting medical errors would contribute to a better system. Most hospitals depend on nurses to discover medication errors because they are considered to be the sources of these errors, as contributors or mere observers, consequently, the nurse’s perception of medication errors and what needs to be done is a vital feature to reducing incidences of medication errors. We sought to explore knowledge among nurses on medical errors and factors affecting or hindering reporting of medical errors among nurses working at the emergency unit, KNH. Critical care nurses are faced with many barriers to completing incident reports on medication errors. One of these barriers which contribute to underreporting is a lack of education and/or knowledge regarding medication errors and the reporting process. This study, therefore, sought to determine the availability and the use of reporting systems for medical errors in critical care unity. It also sought to establish nurses’ perception regarding medical errors and reporting and document factors facilitating timely identification and reporting of medical errors in critical care settings. Methods: The study used cross-section study design to collect data from 76 critical care nurses from Kenyatta Teaching & Research National Referral Hospital, Kenya. Data analysis and results is ongoing. By October 2022, we will have analysis, results, discussions, and recommendations of the study for purposes of the conference in 2023

Keywords: errors, medical, kenya, nurses, safety

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: L. M. Hayyan ul Haq, Lalu Sabardi

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This research is aimed at exploring an appropriate regulatory model in coherencing a diametrical interest between the state, Adat legal community, and private interests in utilising and optimizing land in Indonesia. This work is also highly relevant to coherencing the obligation of the state to respect, to fulfill and to protect the fundamental rights of people, especially to protect the communal or adat community rights to the land. In visualizing those ideas, this research will use the normative legal research to elaborate the normative problem in land use, as well as redesigning and creating an appropriate regulatory model in bridging and protecting all interest parties, especially, the state, Adat legal community, and private parties. In addition, it will also employ an empirical legal research for identifying some operational problems in protecting and optimising the land. In detail, this research will not only identify the problems at the normative level, such as conflicted norms, the absence of the norms, and the unclear norm in land law, but also the problems at operational level, such as institutional relationship in managing the land use. At the end, this work offers an appropriate regulatory model at the systems level, which covers value and norms in land use, as well as the appropriate mechanism in managing the utilization of the land for the state, Adat legal community, and private sector. By manifesting this objective, the government will not only fulfill its obligation to regulate the land for people and private, but also to protect the fundamental rights of people, as mandated by the Indonesian 1945 Constitution.

Keywords: adat community rights, fundamental rights, investment, land law, private sector

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Rosita Kanapeckaite

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The modern operational military environment is a combination of factors such as change, uncertainty, complexity and ambiguity. Stiehm (2002) refers to such situations as VUCA situations. VUCA is an acronym commonly used to describe the volatility, uncertainty, complexity and ambiguity of various situations and conditions. Increasingly fast-paced military operations require military personnel to demonstrate readiness and resilience under stressful conditions in order to maintain the optimum cognitive and physical performance necessary to achieve success. Military resilience can be defined as the ability to cope with the negative effects of setbacks and associated stress on military performance and combat effectiveness. In the volatile, uncertain, complex and ambiguous modern operational environment, both current and future operations require and place a higher priority on enhancing and maintaining troop readiness and resilience to win decisively in multidimensional combat. This paper explores the phenomenon of soldiers' psychological resilience, theories of leadership, and commitment to the organisation. The aim of the study is to examine the relationship between soldiers' psychological resilience, leadership style and commitment to the organisation. The study involved 425 professional soldiers, the research method was a questionnaire survey. The instruments used were measures of psychological resilience, leadership styles and commitment to the organisation. Results: transformational leadership style predicts higher psychological resilience, and psychologically resilient professional servicemen are more committed to the organisation. The study confirms the importance of soldiers' psychological resilience for their commitment to the organisation. The paper also discusses practical applications.

Keywords: resilience, commitment, solders, leadership style

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Authors: Asadullah I. Qazi, Mansoor Ahsan, Zahir Ashraf, Uzair Ahmad

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System identification of an Unmanned Aerial Vehicle (UAV), to acquire its mathematical model, is a significant step in the process of aircraft flight automation. The need for reliable mathematical model is an established requirement for autopilot design, flight simulator development, aircraft performance appraisal, analysis of aircraft modifications, preflight testing of prototype aircraft and investigation of fatigue life and stress distribution etc.  This research is aimed at system identification of a fixed wing UAV by means of specifically designed flight experiment. The purposely designed flight maneuvers were performed on the UAV and aircraft states were recorded during these flights. Acquired data were preprocessed for noise filtering and bias removal followed by parameter estimation of longitudinal dynamics transfer functions using MATLAB system identification toolbox. Black box identification based transfer function models, in response to elevator and throttle inputs, were estimated using least square error   technique. The identification results show a high confidence level and goodness of fit between the estimated model and actual aircraft response.

Keywords: fixed wing UAV, system identification, black box modeling, longitudinal dynamics, least square error

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Authors: A. Fettahoglu

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Theory of viscoelasticity is used by many researchers to represent behavior of many materials such as pavements on roads or bridges. Several researches used analytical methods and rheology to predict the material behaviors of simple models. Today, more complex engineering structures are analyzed using Finite Element Method, in which material behavior is embedded by means of three dimensional viscoelastic material laws. As a result, structures of unordinary geometry and domain like pavements of bridges can be analyzed by means of Finite Element Method and three dimensional viscoelastic equations. In the scope of this study, rheological models embedded in ANSYS, namely, generalized Maxwell elements and Prony series, which are two methods used by ANSYS to represent viscoelastic material behavior, are presented explicitly. Subsequently, a practical problem, which has an analytical solution given in literature, is used to verify the applicability of viscoelasticity tool embedded in ANSYS. Finally, amount of error in the results of ANSYS is compared with the analytical results to indicate the influence of used method and time step size.

Keywords: generalized Maxwell model, finite element method, prony series, time step size, viscoelasticity

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Authors: Luis Guiliana, Andrea Osorio

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The lack of comprehension of the reservoir geomechanics conditions may cause operational problems that cost to the industry billions of dollars per year. The drilling operations at the Ceuta Field, Area 2 South, Maracaibo Lake, have been very expensive due to problems associated with drilling. The principal objective of this investigation is to develop a 3D geomechanical model in this area, in order to optimize the future drillings in the field. For this purpose, a 1D geomechanical model was built at first instance, following the workflow of the MEM (Mechanical Earth Model), this consists of the following steps: 1) Data auditing, 2) Analysis of drilling events and structural model, 3) Mechanical stratigraphy, 4) Overburden stress, 5) Pore pressure, 6) Rock mechanical properties, 7) Horizontal stresses, 8) Direction of the horizontal stresses, 9) Wellbore stability. The 3D MEM was developed through the geostatistic model of the Eocene C-SUP VLG-3676 reservoir and the 1D MEM. With this data the geomechanical grid was embedded. The analysis of the results threw, that the problems occurred in the wells that were examined were mainly due to wellbore stability issues. It was determined that the stress field change as the stratigraphic column deepens, it is normal to strike-slip at the Middle Miocene and Lower Miocene, and strike-slipe to reverse at the Eocene. In agreement to this, at the level of the Eocene, the most advantageous direction to drill is parallel to the maximum horizontal stress (157º). The 3D MEM allowed having a tridimensional visualization of the rock mechanical properties, stresses and operational windows (mud weight and pressures) variations. This will facilitate the optimization of the future drillings in the area, including those zones without any geomechanics information.

Keywords: geomechanics, MEM, drilling, stress

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

Procedia PDF Downloads 134