Search results for: bi-level optimization model

Authors: Phawin Sangsuvan, Chutimet Srinilta

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This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function.

Keywords: character recognition, histogram projection, particle swarm optimization, pattern recognition techniques

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Authors: F. Erdal, E. Dogan, F. E. Uz

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In this study, firefly optimization based optimum design algorithm is presented for the grillage systems. Naming of the algorithm is derived from the fireflies, whose sense of movement is taken as a model in the development of the algorithm. Fireflies’ being unisex and attraction between each other constitute the basis of the algorithm. The design algorithm considers the displacement and strength constraints which are implemented from LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Construction). It selects the appropriate W (Wide Flange)-sections for the transverse and longitudinal beams of the grillage system among 272 discrete W-section designations given in LRFD-AISC so that the design limitations described in LRFD are satisfied and the weight of the system is confined to be minimal. Number of design examples is considered to demonstrate the efficiency of the algorithm presented.

Keywords: firefly algorithm, steel grillage systems, optimum design, stochastic search techniques

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Authors: Naci Büyükkaracığan

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Artificial neural networks (ANN) is an electrical model based on the human brain nervous system and working principle. Artificial neural networks have been the subject of an active field of research that has matured greatly over the past 55 years. ANN now is used in many fields. But, it has been viewed that artificial neural networks give better results in particular optimization and control systems. There are requirements of optimization and control system in many of the area forming the subject of civil engineering applications. In this study, the first artificial intelligence systems are widely used in the solution of civil engineering systems were examined with the basic principles and technical aspects. Finally, the literature reviews for applications in the field of civil engineering were conducted and also artificial intelligence techniques were informed about the study and its results.

Keywords: artificial neural networks, civil engineering, Fuzzy logic, statistics

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Authors: Sungho Kim, Dae Shik Kim, Jong Min Lee

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Fluid catalytic cracking (FCC) process is one of the most important process in modern refinery industry. This paper focuses on the fluid catalytic cracking (FCC) process. As the FCC process is difficult to model well, due to its non linearities and various interactions between its process variables, rigorous process modeling of whole FCC plant is demanded for control and plant-wide optimization of the plant. In this study, a process design for the FCC plant includes riser reactor, main fractionator, and gas processing unit was developed. A reactor model was described based on four-lumped kinetic scheme. Main fractionator, gas processing unit and other process units are designed to simulate real plant data, using a process flow sheet simulator, Aspen PLUS. The custom reactor model was integrated with the process flow sheet simulator to develop an integrated process model.

Keywords: fluid catalytic cracking, simulation, plant data, process design

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Authors: Arash Parehkar, S. Jamshid Mousavi, Shoubo Bayazidi, Vahid Karami, Laleh Shahidi, Arash Azaranfar, Ali Moridi, M. Shabakhti, Tayebeh Ariyan, Mitra Tofigh, Kaveh Masoumi, Alireza Motahari

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Water resource systems modelling have constantly been a challenge through history for human being. As the innovative methodological development is evolving alongside computer sciences on one hand, researches are likely to confront more complex and larger water resources systems due to new challenges regarding increased water demands, climate change and human interventions, socio-economic concerns, and environment protection and sustainability. In this research, an automatic calibration scheme has been applied on the Gilan’s large-scale water resource model using mathematical programming. The water resource model’s calibration is developed in order to attune unknown water return flows from demand sites in the complex Sefidroud irrigation network and other related areas. The calibration procedure is validated by comparing several gauged river outflows from the system in the past with model results. The calibration results are pleasantly reasonable presenting a rational insight of the system. Subsequently, the unknown optimized parameters were used in a basin-scale linear optimization model with the ability to evaluate the system’s performance against a reduced inflow scenario in future. Results showed an acceptable match between predicted and observed outflows from the system at selected hydrometric stations. Moreover, an efficient operating policy was determined for Sefidroud dam leading to a minimum water shortage in the reduced inflow scenario.

Keywords: auto-calibration, Gilan, large-scale water resources, simulation

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Authors: B. Sellami, Y. Laskri, M. Belloufi

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Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, a new family of conjugate gradient method is proposed for unconstrained optimization. This method includes the already existing two practical nonlinear conjugate gradient methods, which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the Wolfe conditions. The numerical experiments are done to test the efficiency of the new method, which implies the new method is promising. In addition the methods related to this family are uniformly discussed.

Keywords: conjugate gradient method, global convergence, line search, unconstrained optimization

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Authors: M. S. Maglasang, S. O. Palacio, L. P. Ogdoc

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Business Process Outsourcing has been one of the fastest growing and emerging industry in the Philippines today. Unlike most of the contact service centers, more popularly known as "call centers", The BPO Industry’s primary outsourced service is performing audits of the global clients' logistics. As a service industry, manpower is considered as the most important yet the most expensive resource in the company. Because of this, there is a need to maximize the human resources so people are effectively and efficiently utilized. The main purpose of the study is to optimize the current manpower resources through effective distribution and assignment of different types of bills to the different skill-level of data entry operators. The assignment model parameters include the average observed time matrix gathered from through time study, which incorporates the learning curve concept. Subsequently, a simulation model was made to duplicate the arrival rate of demand which includes the different batches and types of bill per day. Next, a mathematical linear programming model was formulated. Its objective is to minimize direct labor cost per bill by allocating the different types of bills to the different skill-levels of operators. Finally, a hypothesis test was done to validate the model, comparing the actual and simulated results. The analysis of results revealed that the there’s low utilization of effective capacity because of its failure to determine the product-mix, skill-mix, and simulated demand as model parameters. Moreover, failure to consider the effects of learning curve leads to overestimation of labor needs. From 107 current number of operators, the proposed model gives a result of 79 operators. This results to an increase of utilization of effective capacity to 14.94%. It is recommended that the excess 28 operators would be reallocated to the other areas of the department. Finally, a manpower capacity planning model is also recommended in support to management’s decisions on what to do when the current capacity would reach its limit with the expected increasing demand.

Keywords: optimization modelling, linear programming, simulation, time and motion study, capacity planning

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Khaled S. Abdallah, Abdel-Aziz M. Mohamed

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Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.

Keywords: modeling, truck rental, supply chains management.

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Authors: Héctor Rivera-Gómez, Eva Selene Hernández-Gress, Oscar Montaño-Arango, Jose Ramon Corona-Armenta

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This research studies the joint production, maintenance and subcontracting control policy for an unreliable deteriorating manufacturing system. Production activities are controlled by a derivation of the Hedging Point Policy, and given that the system is subject to deterioration, it reduces progressively its capacity to satisfy product demand. Multiple deterioration effects are considered, reflected mainly in the quality of the parts produced and the reliability of the machine. Subcontracting is available as support to satisfy product demand; also overhaul maintenance can be conducted to reduce the effects of deterioration. The main objective of the research is to determine simultaneously the production, maintenance and subcontracting rate which minimize the total incurred cost. A stochastic dynamic programming model is developed and solved through a simulation-based approach composed of statistical analysis and optimization with the response surface methodology. The obtained results highlight the strong interactions between production, deterioration and quality which justify the development of an integrated model. A numerical example and a sensitivity analysis are presented to validate our results.

Keywords: subcontracting, optimal control, deterioration, simulation, production planning

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Authors: Nur Azzammudin Rahmat, Ismail Musirin, Ahmad Farid Abidin

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Economic load dispatch is performed by the utilities in order to determine the best generation level at the most feasible operating cost. In order to guarantee satisfying energy delivery to the consumer, a precise calculation of generation level is required. In order to achieve accurate and practical solution, several considerations such as prohibited operating zones, valve-point effect and ramp-rate limit need to be taken into account. However, these considerations cause the optimization to become complex and difficult to solve. This research focuses on the valve-point effect that causes ripple in the fuel-cost curve. This paper also proposes Differential Evolution Immunized Ant Colony Optimization (DEIANT) in solving economic load dispatch problem with valve-point effect. Comparative studies involving DEIANT, EP and ACO are conducted on IEEE 30-Bus RTS for performance assessments. Results indicate that DEIANT is superior to the other compared methods in terms of calculating lower operating cost and power loss.

Keywords: ant colony optimization (ACO), differential evolution (DE), differential evolution immunized ant colony optimization (DEIANT), economic load dispatch (ELD)

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Authors: Edward Hage, Pieter Lietaert, Gabriel Abedrabbo

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Musculoskeletal disorders are an important category of work-related diseases. They are often caused by working in non-ergonomic postures and are preventable with proper workplace design, possibly including human-machine collaboration. This paper presents a methodology and a supporting software prototype to design a simple assembly cell with minimal ergonomic risk. The methodology helps to determine the optimal position and orientation of workpieces and workplace resources for specific operator assembly actions. The methodology is tested on an industrial use case: a collaborative robot (cobot) assisted assembly of a clamping device. It is shown that the automated methodology results in a workplace design with significantly reduced ergonomic risk to the operator compared to a manual design of the cell.

Keywords: ergonomics optimization, design for ergonomics, workplace design, pose generation

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Authors: Zaid Almahmoud, Rana Mahmoud

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To mitigate the environmental impacts of throwing away plastic waste, there has been a recent interest in manufacturing and producing biodegradable plastics. Here, we study a new class of biodegradable plastics which are mixed with external natural additives, including catalytic additives that lead to a successful degradation of the resulting material. To recommend the best alternative among multiple materials, we propose a multi-objective AI model that evaluates the material against multiple objectives given the material properties. As a proof of concept, the AI model was implemented in an expert system and evaluated using multiple materials. Our findings showed that Polyethylene Terephalate is potentially the best biodegradable plastic substitute based on its material properties. Therefore, it is recommended that governments shift the attention to the use of Polyethylene Terephalate in the manufacturing of bottles to gain a great environmental and sustainable benefits.

Keywords: plastic bottles, expert systems, multi-objective model, biodegradable substitutes

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Authors: Yalong Jiang, Zheru Chi

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

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

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Authors: Saria Abed, Tahar Khir, Ammar Ben Brahim

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A thermo-exergy optimization of a gas turbine cycle with two different regenerator designs is established. A comparison was made between the performance of the two regenerators and their roles in improving the cycle efficiencies. The effect of operational parameters (the pressure ratio of the compressor, the ambient temperature, excess of air, geometric parameters of the regenerators, etc.) on thermal efficiencies, the exergy efficiencies, and irreversibilities were studied using thermal balances and quantitative exegetic equilibrium for each component and for the whole system. The results are given graphically by using the EES software, and an appropriate discussion and conclusion was made.

Keywords: exergy efficiency, gas turbine, heat transfer, irreversibility, optimization, regenerator, thermal efficiency

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Authors: Ehsan Abdolzadeh, Sanaz Nouri, Siamak Khalaj

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Today WSNs have many applications in different fields like the environment, military operations, discoveries, monitoring operations, and so on. Coverage size and energy consumption are the important challenges that these networks need to face. This paper tries to solve the problem of coverage with a requirement of k-coverage and minimum energy consumption. In order to minimize energy consumption, visual sensor networks have been used that observe and process just those targets that are located in their view direction. As a result, sensor rotations have decreased, and subsequently, energy consumption has been minimized. To solve the problem of coverage particle swarm optimization, coverage optimization has been able to ensure coverage requirement together with minimizing sensor rotations while meeting the problem requirement of k≤14. So energy consumption has decreased, and this could extend the sensors’ lifetime subsequently.

Keywords: K coverage, particle union optimization algorithm, wireless sensor networks, visual sensor networks

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Authors: Leila Jafari, Viliam Makis

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In this paper, the joint optimization of the economic manufacturing quantity (EMQ), safety stock level, and condition-based maintenance (CBM) is presented for a partially observable, deteriorating system subject to random failure. The demand is stochastic and it is described by a Poisson process. The stochastic model is developed and the optimization problem is formulated in the semi-Markov decision process framework. A modification of the policy iteration algorithm is developed to find the optimal policy. A numerical example is presented to compare the optimal policy with the policy considering zero safety stock.

Keywords: condition-based maintenance, economic manufacturing quantity, safety stock, stochastic demand

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Authors: H. E. Keshta, A. A. Ali

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Three phase grid-tied inverters are widely employed in micro-grids (MGs) as interphase between DC and AC systems. These inverters are usually controlled through standard decoupled d–q vector control strategy based on proportional integral (PI) controllers. Recently, advanced meta-heuristic optimization techniques have been used instead of deterministic methods to obtain optimum PI controller parameters. This paper provides a comparative study between the performance of the global Porcellio Scaber algorithm (GPSA) based PI controller and Manta Ray foraging optimization (MRFO) based PI controller.

Keywords: micro-grids, optimization techniques, grid-tied inverter control, PI controller

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Authors: Dana Alghool, Tarek ElMekkawy, Mohamed Haouari, Adel Elomari

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District cooling systems have captured the attentions of many researchers recently due to the enormous benefits offered by such system in comparison with traditional cooling technologies. It is considered a major component of urban cities due to the significant reduction of energy consumption. This paper aims to find the optimal design and operation of district cooling systems by developing a mixed integer linear programming model to minimize the annual total system cost and satisfy the end-user cooling demand. The proposed model is experimented with different cooling demand scenarios. The results of the very high cooling demand scenario are only presented in this paper. A sensitivity analysis on different parameters of the model was performed.

Keywords: Annual Cooling Demand, Compression Chiller, Mathematical Modeling, District Cooling Systems, Optimization

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Authors: Hyun Seop Uhm, Yong Ho Choi, Ji Eun Kim, Young Hoon Lee

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Weapon-target assignment (WTA) is a problem that assigns available launchers to appropriate targets in order to defend assets. Various algorithms for WTA have been developed over past years for both in the static and dynamic environment (denoted by SWTA and DWTA respectively). Due to the problem requirement to be solved in a relevant computational time, WTA has suffered from the solution efficiency. As a result, SWTA and DWTA problems have been solved in the limited situation of the battlefield. In this paper, the general situation under continuous time is considered by Time based Weapon Target Assignment (TWTA) problem. TWTA are studied using the mixed integer programming model, and three heuristic algorithms; decomposed opt-opt, decomposed opt-greedy, and greedy algorithms are suggested. Although the TWTA optimization model works inefficiently when it is characterized by a large size, the decomposed opt-opt algorithm based on the linearization and decomposition method extracted efficient solutions in a reasonable computation time. Because the computation time of the scheduling part is too long to solve by the optimization model, several algorithms based on greedy is proposed. The models show lower performance value than that of the decomposed opt-opt algorithm, but very short time is needed to compute. Hence, this paper proposes an improved method by applying decomposition to TWTA, and more practical and effectual methods can be developed for using TWTA on the battlefield.

Keywords: air and missile defense, weapon target assignment, mixed integer programming, piecewise linearization, decomposition algorithm, military operations research

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Authors: Ashwani Kumar

Abstract:

Abstract- This study presents an analytical modeling approach for analyzing the drain current behavior in Tunnel Field-Effect Transistor (TFET) biosensors used for the detection of DNA biomolecules. The proposed model focuses on elucidating the relationship between the drain current and the presence of DNA biomolecules, taking into account the impact of various device parameters and biomolecule characteristics. Through comprehensive analysis, the model offers insights into the underlying mechanisms governing the sensing performance of TFET biosensors, aiding in the optimization of device design and operation. A non-local tunneling model is incorporated with other essential models to accurately trace the simulation and modeled data. An experimental validation of the model is provided, demonstrating its efficacy in accurately predicting the drain current response to DNA biomolecule detection. The sensitivity attained from the analytical model is compared and contrasted with the ongoing research work in this area.

Keywords: biosensor, double-gate TFET, DNA detection, drain current modeling, sensitivity

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Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

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Authors: Karthikeyan Kalirajan, Ashok Joshi

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An optimization problem is to setup to maximize the terminal kinetic energy of a maneuverable reentry vehicle (MaRV). The target location, the impact angle is given as constraints. The MaRV uses an explicit guidance law called Vector guidance. This law has two gains which are taken as decision variables. The problem is to find the optimal value of these gains which will result in minimum miss distance and impact angle error. Using a simple 3DOF non-rotating flat earth model and Lockheed martin HP-MARV as the reentry vehicle, the nature of solutions of the optimization problem is studied. This is achieved by carrying out a parametric study for a range of closed loop gain values and the corresponding impact angle error and the miss distance values are generated. The results show that there are well defined lower and upper bounds on the gains that result in near optimal terminal guidance solution. It is found from this study, that there exist common permissible regions (values of gains) where all constraints are met. Moreover, the permissible region lies between flat regions and hence the optimization algorithm has to be chosen carefully. It is also found that, only one of the gain values is independent and that the other dependent gain value is related through a simple straight-line expression. Moreover, to reduce the computational burden of finding the optimal value of two gains, a guidance law called Diveline guidance is discussed, which uses single gain. The derivation of the Diveline guidance law from Vector guidance law is discussed in this paper.

Keywords: Marv guidance, reentry trajectory, trajectory optimization, guidance gain selection

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Authors: Prakash Kattire, Rahul Pathare, Nilesh Tawde

Abstract:

A canopy is an overhead roof structure generally used at the entrance of a building to provide shelter from rain and sun and may also be used for decorative purposes. In this paper, the canopy structure to cover the conveyor line has been studied. Existing most of the canopy structures are made of steel and glass, which makes a heavier structure, so the purpose of this study is to weight and cost optimization of the canopy. To achieve this goal, the materials of construction considered are Polyvinyl chloride (PVC) natural composite, Fiber Reinforced Plastic (FRP), and Structural steel Fe250. Designing and modeling were done in Solid works, whereas Altair Inspire software was used for the optimization of the structure. Through this study, it was found that there is a total 10% weight reduction in the structure with sufficient reserve for structural strength.

Keywords: canopy, composite, FRP, PVC

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Authors: Mohamed Ghorab

Abstract:

Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO₂ optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO₂ emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO₂ emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.  

Keywords: distributed energy resources, network energy system, optimization, microgeneration system

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Authors: Ali Nadi, Ali Edrissi

Abstract:

Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Sai Siddharth S., Prasanna Kumar G. M., Alagarsamy R.

Abstract:

This research introduces an alternative approach to urban road maintenance by utilizing Blended Wing Body (BWB) design and Vertical Takeoff and Landing (VTOL) drones. The integration of this aerospace innovation, combining blended wing efficiency with VTOL maneuverability, aims to optimize fuel consumption and explore versatile applications in solving urban problems. A few problems are discussed along with optimization of the design and comparative study with other drone configurations.

Keywords: design optimization, CFD, CAD, VTOL, blended wing body

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

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Authors: Tarek Litim, Ouahiba Taamallah

Abstract:

The present paper is an investigation of the effect of burnishing on the surface integrity of a component made of 15NiCr6 steel. This work shows a statistical study based on regression, and Taguchi's design has allowed the development of mathematical models to predict the output responses as a function of the technological parameters studied. The response surface methodology (RSM) showed a simultaneous influence of the burnishing parameters and observe the optimal processing parameters. ANOVA analysis of the results resulted in the validation of the prediction model with a determination coefficient R=90.60% and 92.41% for roughness and hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=10kgf, i=3passes, and f=0.074mm/rev, which favours minimum roughness and maximum hardness. The result was validated by the desirability of D= (0.99 and 0.95) for roughness and hardness, respectively.

Keywords: 15NiCr6 steel, burnishing, surface integrity, Taguchi, RSM, ANOVA

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Authors: Supriya Chatla, Anjana Male, Srikala Kamireddy

Abstract:

L-asparaginase (E.C.3.5.1.1) is an anti-cancer enzyme that has been purified and characterized for decades to study and evaluate its anti-carcinogenic activity against Hodgkin’s lymphoma. The present investigation deals with screening, isolation and optimization of L-asparaginase giving fungal strain of soil samples from different areas of AP, India. L-Aspariginase activity was estimated on the basis of the pink color surrounding the growing colony. A total of 132 colonies were screened and isolated from different samples. Based on the zone diameter, L-asparaginase activity is determined, L- asparaginase activity is optimized at 28oc and Immobilized Aspariginase had more potency than the free enzymes.

Keywords: aspariginase, anticancer enzyme, Isolation, optimization

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