Development of an Explainable Model for a Gluconic Acid Bioreactor and Profit Maximization through Grey Wolf Optimizer Trained Artificial Neural Network Technique
This document discusses developing an explainable artificial neural network model for optimizing a gluconic acid bioreactor process. It aims to 1) use a grey wolf optimizer trained ANN approach to model the complex bioreactor system, 2) convert the ANN model into an explainable closed-form equation to provide insight into the underlying reactor physics, and 3) optimize the model to maximize gluconic acid yield and profitability using an evolutionary algorithm. Artificial intelligence techniques like ANNs are effective for modeling complicated bioprocesses but provide "black box" solutions that lack explainability. This study develops a general methodology to increase the explainability and acceptability of an ANN model for engineering applications like bioreactor optimization
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...IJERD Editor
The rising demand for high quality homogenous castings necessitate that vast amount of
manufacturing knowledge be incorporated in manufacturing systems. Rotary furnace involves several critical
parameters like excess air, flame temperature, rotational speed of the furnace drum, melting time, preheat air
temperature, fuel consumption and melting rate of the molten metal which should be controlled throughout the
melting process. A complex relationship exists between these manufacturing parameters and hence there is a
need to develop models which can capture this complex interrelationship and enable fast computation. In the
present work, we propose a generic approach where the applicability and effectiveness of neural network in
function approximation is used for rapid estimation of melting rate and they are integrated into the framework
of genetic evolutionary algorithm to form a neuro-genetic optimization technique. A neural network model is
trained with the experimental results. The results indicate that the heuristic converges to better solutions rapidly
as it provides the values of various process parameters for optimizing the objective in a single run and thus
assists for the improvement of quality in development of sound parts
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...IJERD Editor
The rising demand for high quality homogenous castings necessitate that vast amount of
manufacturing knowledge be incorporated in manufacturing systems. Rotary furnace involves several critical
parameters like excess air, flame temperature, rotational speed of the furnace drum, melting time, preheat air
temperature, fuel consumption and melting rate of the molten metal which should be controlled throughout the
melting process. A complex relationship exists between these manufacturing parameters and hence there is a
need to develop models which can capture this complex interrelationship and enable fast computation. In the
present work, we propose a generic approach where the applicability and effectiveness of neural network in
function approximation is used for rapid estimation of melting rate and they are integrated into the framework
of genetic evolutionary algorithm to form a neuro-genetic optimization technique. A neural network model is
trained with the experimental results. The results indicate that the heuristic converges to better solutions rapidly
as it provides the values of various process parameters for optimizing the objective in a single run and thus
assists for the improvement of quality in development of sound parts
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Energy management system for distribution networks integrating photovoltaic ...IJECEIAES
The concept of the energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of purchased energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS) and entropy-TOPSIS. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of purchased energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.
Determining the Pareto front of distributed generator and static VAR compens...IJECEIAES
The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.
The Evolving Landscape of Semiconductor Manufacturing to Mitigate Yield Losse...yieldWerx Semiconductor
Semiconductor manufacturing, often described as a labyrinth of complex and multi-layered processes, is central to the production of integrated circuits. These circuits, already intricate, are becoming progressively more complex with each technological leap. This evolution intensifies the requirement for robust performance metrics, such as defect rate, semiconductor yield improvement, and cycle time. Through rigorous monitoring and analysis of these parameters, manufacturers can make significant enhancements to their performance, yielding a substantial impact on operational efficiency and profitability. This detailed exposition presents a comprehensive examination of yield modeling, dynamic capacity re-allocation mechanisms, yield competitiveness, and yield prediction models, offering invaluable insights for the semiconductor manufacturing industry.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
IMPROVED SWARM INTELLIGENCE APPROACH TO MULTI OBJECTIVE ED PROBLEMSSuganthi Thangaraj
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Abstract- In this article the authors, based on the VisiMix Software, the experience of VisiMix users and personal knowledge from more than ten years of experience using VisiMix for API, Fine Chemicals and others, processes simulation, show a Method for Scale Down – Scale Up of Batch – Semi Batch operations built under Hydrodynamics study of the Mixing procedure in the reactor system. The use of the recommended method will offer the user the possibility to achieve the best results during production stage with saving among time and currency, and at the same time increasing the knowledge of the performed process. Several examples at the end of the article show the benefits of the proposed VisiMix Method Loops for Scale Down - Scale Up and Hydrodynamics Considerations.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Chemical production is a result of several chemical reactions and purification steps. Purification steps and processes yield are a direct function of the level of understanding of the reaction system. Reaction quality results have a tremendous impact in separation technology.
Chemical production is frequently performed on stirred vessels that are operated at batch or semi-batch configuration. The choice process configuration is determined at the development stage of the project. Therefore, if the chemical reaction and mixing are not well understood, wrong selections will be adopted in the process development
A progressive domain expansion method for solving optimal control problemTELKOMNIKA JOURNAL
Electricity generation at the hydropower stations in Nigeria has been below the expected value. While the hydro stations have a capacity to generate up to 2,380 MW, the daily average energy generated in 2017 was estimated at around 846 MW. A factor responsible for this is the lack of a proper control system to manage the transfer of resources between the cascaded Kainji-Jebba Hydropower stations operating in tandem. This paper addressed the optimal regulation of the operating head of the Jebba hydropower reservoir in the presence of system constraints, flow requirement and environmental factors that are weather-related. The resulting two-point boundary value problem was solved using the progressive expansion of domain technique as against the shooting or multiple shooting techniques. The results provide the optimal inflow required to keep the operating head of the Jebba reservoir at a nominal level, hence ensuring that the maximum number of turbo-alternator units are operated.
Similar to Development of an Explainable Model for a Gluconic Acid Bioreactor and Profit Maximization through Grey Wolf Optimizer Trained Artificial Neural Network Technique
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Energy management system for distribution networks integrating photovoltaic ...IJECEIAES
The concept of the energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of purchased energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS) and entropy-TOPSIS. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of purchased energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.
Determining the Pareto front of distributed generator and static VAR compens...IJECEIAES
The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.
The Evolving Landscape of Semiconductor Manufacturing to Mitigate Yield Losse...yieldWerx Semiconductor
Semiconductor manufacturing, often described as a labyrinth of complex and multi-layered processes, is central to the production of integrated circuits. These circuits, already intricate, are becoming progressively more complex with each technological leap. This evolution intensifies the requirement for robust performance metrics, such as defect rate, semiconductor yield improvement, and cycle time. Through rigorous monitoring and analysis of these parameters, manufacturers can make significant enhancements to their performance, yielding a substantial impact on operational efficiency and profitability. This detailed exposition presents a comprehensive examination of yield modeling, dynamic capacity re-allocation mechanisms, yield competitiveness, and yield prediction models, offering invaluable insights for the semiconductor manufacturing industry.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
IMPROVED SWARM INTELLIGENCE APPROACH TO MULTI OBJECTIVE ED PROBLEMSSuganthi Thangaraj
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Abstract- In this article the authors, based on the VisiMix Software, the experience of VisiMix users and personal knowledge from more than ten years of experience using VisiMix for API, Fine Chemicals and others, processes simulation, show a Method for Scale Down – Scale Up of Batch – Semi Batch operations built under Hydrodynamics study of the Mixing procedure in the reactor system. The use of the recommended method will offer the user the possibility to achieve the best results during production stage with saving among time and currency, and at the same time increasing the knowledge of the performed process. Several examples at the end of the article show the benefits of the proposed VisiMix Method Loops for Scale Down - Scale Up and Hydrodynamics Considerations.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Chemical production is a result of several chemical reactions and purification steps. Purification steps and processes yield are a direct function of the level of understanding of the reaction system. Reaction quality results have a tremendous impact in separation technology.
Chemical production is frequently performed on stirred vessels that are operated at batch or semi-batch configuration. The choice process configuration is determined at the development stage of the project. Therefore, if the chemical reaction and mixing are not well understood, wrong selections will be adopted in the process development
A progressive domain expansion method for solving optimal control problemTELKOMNIKA JOURNAL
Electricity generation at the hydropower stations in Nigeria has been below the expected value. While the hydro stations have a capacity to generate up to 2,380 MW, the daily average energy generated in 2017 was estimated at around 846 MW. A factor responsible for this is the lack of a proper control system to manage the transfer of resources between the cascaded Kainji-Jebba Hydropower stations operating in tandem. This paper addressed the optimal regulation of the operating head of the Jebba hydropower reservoir in the presence of system constraints, flow requirement and environmental factors that are weather-related. The resulting two-point boundary value problem was solved using the progressive expansion of domain technique as against the shooting or multiple shooting techniques. The results provide the optimal inflow required to keep the operating head of the Jebba reservoir at a nominal level, hence ensuring that the maximum number of turbo-alternator units are operated.
Similar to Development of an Explainable Model for a Gluconic Acid Bioreactor and Profit Maximization through Grey Wolf Optimizer Trained Artificial Neural Network Technique (20)
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Student information management system project report ii.pdf
Development of an Explainable Model for a Gluconic Acid Bioreactor and Profit Maximization through Grey Wolf Optimizer Trained Artificial Neural Network Technique