Search results for: dynamical model

Authors: Fulya Aydın, Ayşe Kuleyin

Abstract:

The purpose of this study is to investigate the capacity of natural Turkish zeolite for NH4-N removal from landfill leachate. The effects of modification and initial concentration on the removal of NH4-N from leachate were also investigated. The kinetics of adsorption of NH4-N has been discussed using three kinetic models, i.e., the pseudo-second order model, the Elovich equation, the intraparticle diffuion model. Kinetic parameters and correlation coefficients were determined. Equilibrium isotherms for the adsorption of NH4-N were analyzed by Langmuir, Freundlich and Tempkin isotherm models. Langmuir isotherm model was found to best represent the data for NH4-N.

Keywords: Leachate, Ammonium, zeolite

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Authors: Rajamani Doraiswami, Lahouari Cheded

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Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system.

Keywords: Keywords—Identification, linear parameter-varying systems, least-squares estimation, fault diagnosis, Kalman filter, emulators

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Authors: Faraj Farhat El Dabee, Rajab Abdullah Hokoma

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Supply Chain Management (SCM) is the integration between manufacturer, transporter and customer in order to form one seamless chain that allows smooth flow of raw materials, information and products throughout the entire network that help in minimizing all related efforts and costs. The main objective of this paper is to develop a model that can accept a specified number of spare-parts within the supply chain, simulating its inventory operations throughout all stages in order to minimize the inventory holding costs, base-stock, safety-stock, and to find the optimum quantity of inventory levels, thereby suggesting a way forward to adapt some factors of Just-In-Time to minimizing the inventory costs throughout the entire supply chain. The model has been developed using Micro- Soft Excel & Visual Basic in order to study inventory allocations in any network of the supply chain. The application and reproducibility of this model were tested by comparing the actual system that was implemented in the case study with the results of the developed model. The findings showed that the total inventory costs of the developed model are about 50% less than the actual costs of the inventory items within the case study.

Keywords: Holding Costs, Inventory, JIT, Modeling, SCM

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Authors: Zhaohui Wen

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In this paper, a food chain model with Holling type II functional response on time scales is investigated. By using the Mawhin-s continuation theorem in coincidence degree theory, sufficient conditions for existence of periodic solutions are obtained.

Keywords: Periodic solutions, food chain model, coincidence degree, time scales.

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the CPU, RAM, and ROM memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles.

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Authors: Thanh-Duoc Phan, Saden H. Zahiri, S. H. Masood, Mahnaz Jahedi

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In this article, using finite element analysis (FEA) and an X-ray diffractometer (XRD), cold-sprayed titanium particles on a steel substrate is investigated in term of cooling time and the development of residual strains. Three cooling-down models of sprayed particles after deposition stage are simulated and discussed: the first model (m1) considers conduction effect to the substrate only, the second model (m2) considers both conduction as well as convection effect to the environment, and the third model (m3) which is the same as the second model but with the substrate heated to a near particle temperature before spraying. Thereafter, residual strains developed in the third model is compared with the experimental measurement of residual strains, which involved a Bruker D8 Advance Diffractometer using CuKa radiation (40kV, 40mA) monochromatised with a graphite sample monochromator. For deposition conditions of this study, a good correlation was found to exist between the FEA results and XRD measurements of residual strains.

Keywords: cold gas dynamic spray, X-ray diffraction, explicit finite element analysis, residual strain, titanium, particle impact, deformation behavior.

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Authors: A. Nuchitprasittichai, N. Lerdritsirikoon, T. Khamsing

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Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO₂ liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments.

Keywords: Central composite design, CO2 liquefaction, Latin Hypercube Sampling, simulation – based optimization.

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Authors: Abdel Hamid Ajbar, Emad Ali

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Saudi Arabia is an arid country which depends on costly desalination plants to satisfy the growing residential water demand. Prediction of water demand is usually a challenging task because the forecast model should consider variations in economic progress, climate conditions and population growth. The task is further complicated knowing that Mecca city is visited regularly by large numbers during specific months in the year due to religious occasions. In this paper, a neural networks model is proposed to handle the prediction of the monthly and yearly water demand for Mecca city, Saudi Arabia. The proposed model will be developed based on historic records of water production and estimated visitors- distribution. The driving variables for the model include annuallyvarying variables such as household income, household density, and city population, and monthly-varying variables such as expected number of visitors each month and maximum monthly temperature.

Keywords: Water demand forecast; Neural Networks model; water resources management; Saudi Arabia.

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Authors: Syeda Maria Ali Shah, Asif Mansoor, Talat Sharafat Rehmani, Safia Mirza

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The stock market of any country acts as a predictor of the economy. The spread of the COVID-19 pandemic has severely impacted the global financial markets. Besides, it has also critically affected the economy of Pakistan. In this study, we consider the role of the Karachi Stock Exchange (KSE) with regard to the Pakistan Stock Exchange and quantify the impact on macroeconomic variables in presence of COVID-19. The suitable macroeconomic variables are used to quantify the impact of COVID-19 by developing the stochastic model. The sufficiency of the computed model is attained by means of available techniques in the literature. The estimated equations are used to forecast the impact of pandemic on macroeconomic variables. The constructed model can help the policymakers take counteractive measures for restricting the influence of viruses on the Karachi Stock Market.

Keywords: COVID-19, Karachi Stock Market, macroeconomic variables, stochastic model, forecasting.

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Authors: J. C. Moctezuma, V. Breña-Medina, Jose Luis Nunez-Yanez, Joseph P. McGeehan

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In this work we make a bifurcation analysis for a single compartment representation of Traub model, one of the most important conductance-based models. The analysis focus in two principal parameters: current and leakage conductance. Study of stable and unstable solutions are explored; also Hop-bifurcation and frequency interpretation when current varies is examined. This study allows having control of neuron dynamics and neuron response when these parameters change. Analysis like this is particularly important for several applications such as: tuning parameters in learning process, neuron excitability tests, measure bursting properties of the neuron, etc. Finally, a hardware implementation results were developed to corroborate these results.

Keywords: Traub model, Pinsky-Rinzel model, Hopf bifurcation, single-compartment models, Bifurcation analysis, neuron modeling.

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Authors: S. S. Rwanga, J. M. Ndambuki, Y. Woyessa

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Development of a groundwater management model is an important step in the exploitation and management of any groundwater aquifer as it assists in the long-term sustainable planning of the resource. The current study was conducted in Central Limpopo province of South Africa with the overall objective of determining how much water can be withdrawn from the aquifer without producing nonreversible impacts on the groundwater quantity, hence developing a model which can sustainably protect the aquifer. The development was done through the computation of Groundwater Sustainability Index (GSI). Values of GSI close to unity and above indicated overexploitation. In this study, an index of 0.8 was considered as overexploitation. The results indicated that there is potential for higher abstraction rates compared to the current abstraction rates. GSI approach can be used in the management of groundwater aquifer to sustainably develop the resource and also provides water managers and policy makers with fundamental information on where future water developments can be carried out.

Keywords: Development, groundwater, groundwater sustainability index, model.

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Authors: L. Mirtskhulava, M. Khunjgurua, N. Lomineishvili, K. Bakuria

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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: Exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability.

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Authors: Ridha Ghayoula, Mbarek Traii, Ali Gharsallah

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In this paper, we intend to study the synthesis of the multibeam arrays. The synthesis implementation-s method for this type of arrays permits to approach the appropriated radiance-s diagram. The used approach is based on neural network that are capable to model the multibeam arrays, consider predetermined general criteria-s, and finally it permits to predict the appropriated diagram from the neural model. Our main contribution in this paper is the extension of a synthesis model of these multibeam arrays.

Keywords: Multibeam, modelling, neural networks, synthesis, antennas.

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Authors: Eduardo C. Guardia, Jose W. M. Lima, Afonso H. M. Santos

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This paper presents a nonparametric method to obtain the hazard rate “Bathtub curve” for power system components. The model is a mixture of the three known phases of a component life, the decreasing failure rate (DFR), the constant failure rate (CFR) and the increasing failure rate (IFR) represented by three parametric Weibull models. The parameters are obtained from a simultaneous fitting process of the model to the Kernel nonparametric hazard rate curve. From the Weibull parameters and failure rate curves the useful lifetime and the characteristic lifetime were defined. To demonstrate the model the historic time-to-failure of distribution transformers were used as an example. The resulted “Bathtub curve” shows the failure rate for the equipment lifetime which can be applied in economic and replacement decision models.

Keywords: Bathtub curve, failure analysis, lifetime estimation, parameter estimation, Weibull distribution.

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Authors: T. K. Sriram, Joydip Dhar

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Models based on stage structure have found varied applications in population models. This paper proposes a stage structured model to study the trends in the computer and video game playing population of US. The game paying population is divided into three compartments based on their age group. After simulating the mathematical model, a forecast of the number of game players in each stage as well as an approximation of the average age of game players in future has been made.

Keywords: Age structure, Forecasting, Mathematical modeling, Stage structure.

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Authors: Jin-Bao Zhao, Wei Deng

Abstract:

This paper aims at developing a multilevel fuzzy decision support model for urban rail transit planning schemes in China under the background that China is presently experiencing an unprecedented construction of urban rail transit. In this study, an appropriate model using multilevel fuzzy comprehensive evaluation method is developed. In the decision process, the followings are considered as the influential objectives: traveler attraction, environment protection, project feasibility and operation. In addition, consistent matrix analysis method is used to determine the weights between objectives and the weights between the objectives- sub-indictors, which reduces the work caused by repeated establishment of the decision matrix on the basis of ensuring the consistency of decision matrix. The application results show that multilevel fuzzy decision model can perfectly deal with the multivariable and multilevel decision process, which is particularly useful in the resolution of multilevel decision-making problem of urban rail transit planning schemes.

Keywords: Urban rail transit, planning schemes, multilevel fuzzy decision support model, consistent matrix analysis

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Authors: N. Manavizadeh, A. Dehghani, M. Rabbani

Abstract:

Mixed model assembly lines (MMAL) are a type of production line where a variety of product models similar in product characteristics are assembled. The effective design of these lines requires that schedule for assembling the different products is determined. In this paper we tried to fit the sequencing problem with the main characteristics of make to order (MTO) environment. The problem solved in this paper is a multiple objective sequencing problem in mixed model assembly lines sequencing using weighted Sum Method (WSM) using GAMS software for small problem and an effective GA for large scale problems because of the nature of NP-hardness of our problem and vast time consume to find the optimum solution in large problems. In this problem three practically important objectives are minimizing: total utility work, keeping a constant production rate variation, and minimizing earliness and tardiness cost which consider the priority of each customer and different due date which is a real situation in mixed model assembly lines and it is the first time we consider different attribute to prioritize the customers which help the company to reduce the cost of earliness and tardiness. This mechanism is a way to apply an advance available to promise (ATP) in mixed model assembly line sequencing which is the main contribution of this paper.

Keywords: Available to promise, Earliness & Tardiness, GA, Mixed-Model assembly line Sequencing.

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Authors: F. Nazem, M. Mozaiini, A. Seifi

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The purpose of the present study was to provide a structural model of knowledge management in universities based on organizational climate. The population of the research included all employees of Islamic Azad University (IAU). The sample consisted of 1590 employees selected using stratified and cluster random sampling method. The research instruments were two questionnaires which were administered in 78 IAU branches and education centers: Sallis and Jones’s (2002) Knowledge Management Questionnaire (α= 0.97); and Latwin & Stringer’s (1968) Organizational Climate Questionnaire (α= 0.83). The results of path analysis using LISREL software indicated that dimensions of organizational climate had a direct effect on knowledge management with the indices of 0.94. The model also showed that the factor of support in organizational climate had the highest direct effect on the knowledge management.

Keywords: Knowledge management, Organizational climate, Structural model, Universities.

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Authors: Smajo Bisanovic, Mensur Hajro, Muris Dlakic

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This paper presents one comprehensive modelling approach for maintenance scheduling problem of thermal power units in competitive market. This problem is formulated as a 0/1 mixedinteger linear programming model. Model incorporates long-term bilateral contracts with defined profiles of power and price, and weekly forecasted market prices for market auction. The effectiveness of the proposed model is demonstrated through case study with detailed discussion.

Keywords: Maintenance scheduling, bilateral contracts, market prices, profit.

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Authors: Ali Ghaffari, Mansour Nikkhah Bahrami, Hesam Parsa

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In this paper a comprehensive model of a fossil fueled power plant (FFPP) is developed in order to evaluate the performance of a newly designed turbine follower controller. Considering the drawbacks of previous works, an overall model is developed to minimize the error between each subsystem model output and the experimental data obtained at the actual power plant. The developed model is organized in two main subsystems namely; Boiler and Turbine. Considering each FFPP subsystem characteristics, different modeling approaches are developed. For economizer, evaporator, superheater and reheater, first order models are determined based on principles of mass and energy conservation. Simulations verify the accuracy of the developed models. Due to the nonlinear characteristics of attemperator, a new model, based on a genetic-fuzzy systems utilizing Pittsburgh approach is developed showing a promising performance vis-à-vis those derived with other methods like ANFIS. The optimization constraints are handled utilizing penalty functions. The effect of increasing the number of rules and membership functions on the performance of the proposed model is also studied and evaluated. The turbine model is developed based on the equation of adiabatic expansion. Parameters of all evaluated models are tuned by means of evolutionary algorithms. Based on the developed model a fuzzy PI controller is developed. It is then successfully implemented in the turbine follower control strategy of the plant. In this control strategy instead of keeping control parameters constant, they are adjusted on-line with regard to the error and the error rate. It is shown that the response of the system improves significantly. It is also shown that fuel consumption decreases considerably.

Keywords: Attemperator, Evolutionary algorithms, Fossil fuelled power plant (FFPP), Fuzzy set theory, Gain scheduling

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Authors: Ramin Javadzadeh, Zahra Afsahi, MohammadReza Meybodi

Abstract:

The hybridization of artificial immune system with cellular automata (CA-AIS) is a novel method. In this hybrid model, the cellular automaton within each cell deploys the artificial immune system algorithm under optimization context in order to increase its fitness by using its neighbor-s efforts. The hybrid model CA-AIS is introduced to fix the standard artificial immune system-s weaknesses. The credibility of the proposed approach is evaluated by simulations and it shows that the proposed approach achieves better results compared to standard artificial immune system.

Keywords: Artificial Immune System, Cellular Automat, neighborhood

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Authors: Tarek Abdel Azim Ahmed, Eman Sarhan Shaker

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This research aims to explore how well the extended model of internet commerce adoption (eMICA) model is often used to determine the extent of internet commerce adoption in the travel agencies sector in both Egypt and Kingdom of Saudi Arabia (KSA). The web content analysis method was used to analyze the level of adoption of Egyptian travel agencies and Saudi travel agencies according to data immensely available on their websites. Therefore, each site was categorized according to the phases and levels proposed. In order to achieve this, 120 websites were evaluated by the two authors over a three-month period, from August to October 2020, and then categorized according to the phases and levels of (eMICA). The results show that there are deficiencies in the application of the eMICA model by both KSA and Egyptian travel agencies, generally, updating their websites, the absence of quality certification, offering secure online payment, virtual tours, and videos using Flash animation. In general, the Egyptian companies slightly outperformed the KSA ones in applying eMICA model.

Keywords: e-commerce, eMICA, Internet marketing, travel agencies, websites.

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Authors: Khaoula Hidouri, Ali Benhmidene, Bechir Chouachi

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The purification of brackish seawater becomes a necessity and not a choice against demographic and industrial growth especially in third world countries. Two models can be used in this work: simple solar still and simple solar still coupled with a heat pump. In this research, the productivity of water by Simple Solar Distiller (SSD) and Simple Solar Distiller Hybrid Heat Pump (SSDHP) was determined by the orientation, the use of heat pump, the simple or double glass cover. The productivity can exceed 1.2 L/m²h for the SSDHP and 0.5 L/m²h for SSD model. The result of the global efficiency is determined for two models SSD and SSDHP give respectively 30%, 50%. The internal efficiency attained 35% for SSD and 60% of the SSDHP models. Convective heat coefficient can be determined by attained 2.5 W/m²°C and 0.5 W/m²°C respectively for SSDHP and SSD models.

Keywords: Productivity, efficiency, convective heat coefficient, SSD model, SSDHP model.

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Authors: B.A.Gobin, R.K.Subramanian

Abstract:

This paper gives an overview of the mapping mechanism of SEAM-a methodology for the automatic generation of knowledge models and its mapping onto Java codes. It discusses the rules that will be used to map the different components in the knowledge model automatically onto Java classes, properties and methods. The aim of developing this mechanism is to help in the creation of a prototype which will be used to validate the knowledge model which has been generated automatically. It will also help to link the modeling phase with the implementation phase as existing knowledge engineering methodologies do not provide for proper guidelines for the transition from the knowledge modeling phase to development phase. This will decrease the development overheads associated to the development of Knowledge Based Systems.

Keywords: KBS, OWL, ontology, knowledge models

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Authors: S. Zamir

Abstract:

The modern era has brought with it significant organizational changes. These changes have not bypassed the academic world, and along with the old academic bonds that include a world of knowledge and ethics, academic faculty members are required more than ever not only to survive in the academic world, but also to thrive and flourish and position themselves as modern and opinionated academicians. Based upon the writings of organizational consultants, the article suggests a 10 X C model for cultivating an academic backbone, as well as emphasizing its input to the professional growth of university and college academics: Competence, Calculations of pain & gain, Character, Commitment, Communication, Curiosity, Coping, Courage, Collaboration and Celebration.

Keywords: Academic career, academicians, higher education, the 10xC Model.

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Authors: Satoshi Usami

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Among various HLM techniques, the Multivariate Hierarchical Linear Model (MHLM) is desirable to use, particularly when multivariate criterion variables are collected and the covariance structure has information valuable for data analysis. In order to reflect prior information or to obtain stable results when the sample size and the number of groups are not sufficiently large, the Bayes method has often been employed in hierarchical data analysis. In these cases, although the Markov Chain Monte Carlo (MCMC) method is a rather powerful tool for parameter estimation, Procedures regarding MCMC have not been formulated for MHLM. For this reason, this research presents concrete procedures for parameter estimation through the use of the Gibbs samplers. Lastly, several future topics for the use of MCMC approach for HLM is discussed.

Keywords: Gibbs sampler, Hierarchical Linear Model, Markov Chain Monte Carlo, Multivariate Hierarchical Linear Model

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Authors: S. Panda, J. S. Yadav, N. P. Patidar, C. Ardil

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Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. In this paper both PSO and GA optimization are employed for finding stable reduced order models of single-input- single-output large-scale linear systems. Both the techniques guarantee stability of reduced order model if the original high order model is stable. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example from literature and the results are compared with recently published conventional model reduction technique.

Keywords: Genetic Algorithm, Particle Swarm Optimization, Order Reduction, Stability, Transfer Function, Integral Squared Error.

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Authors: Ahmad Aljaafreh

Abstract:

This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.

Keywords: Classification, decision fusion, fuzzy logic, hidden Markov model

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Authors: Chin S. Y., Radzi, S. N. R., Maharon, I. H., Shafawi, M. A.

Abstract:

A kinetic model for propane dehydrogenation in an industrial moving bed reactor is developed based on the reported reaction scheme. The kinetic parameters and activity constant are fine tuned with several sets of balanced plant data. Plant data at different operating conditions is applied to validate the model and the results show a good agreement between the model predictions and plant observations in terms of the amount of main product, propylene produced. The simulation analysis of key variables such as inlet temperature of each reactor (Tinrx) and hydrogen to total hydrocarbon ratio (H2/THC) affecting process performance is performed to identify the operating condition to maximize the production of propylene. Within the range of operating conditions applied in the present studies, the operating condition to maximize the propylene production at the same weighted average inlet temperature (WAIT) is ΔTinrx1= -2, ΔTinrx2= +1, ΔTinrx3= +1 , ΔTinrx4= +2 and ΔH2/THC= -0.02. Under this condition, the surplus propylene produced is 7.07 tons/day as compared with base case.

Keywords: kinetic model, dehydrogenation, simulation, modeling, propane

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Authors: B. S. Fatoyinbo, D. Stretch, O. T. Amoo, D. Allopi

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This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.

Keywords: Ungauged Basin, Catchment Characteristics Model, Synthetic data, GIS.

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