The Experts below are selected from a list of 153531 Experts worldwide ranked by ideXlab platform
Rajagopalan Srinivasan - One of the best experts on this subject based on the ideXlab platform.
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a knowledge based simulation Optimization Framework and system for sustainable process operations
Computers & Chemical Engineering, 2011Co-Authors: Iskandar Halim, Rajagopalan SrinivasanAbstract:Abstract Design and operation of chemical plants involves a combination of synthesis, analysis and evaluation of alternatives. Such activities have traditionally been driven by economic factors first, followed by engineering, safety and environmental considerations. Recently, chemical companies have embraced the concept of sustainable development, entailing renewable feed materials and energy, non-toxic and biodegradable products, and waste minimization or even elimination at source. In this paper, we introduce a knowledge-based simulation-Optimization Framework for generating sustainable alternatives to chemical processes. The Framework has been developed by combining different process systems engineering methodologies – the knowledge-based approach for identifying the root cause of waste generation, the hierarchical design method for generating alternative designs, sustainability metrics, and multi-objective Optimization – into one coherent simulation-Optimization Framework. This is implemented as a decision-support system using Gensym's G2 and the HYSYS process simulator. We illustrate the Framework and system using the HDA and biodiesel production case studies.
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designing sustainable alternatives for batch operations using an intelligent simulation Optimization Framework
Chemical Engineering Research & Design, 2008Co-Authors: Iskandar Halim, Rajagopalan SrinivasanAbstract:Abstract The drive towards sustainability has compelled the batch process industries to implement the concept of environmentally friendly plants. However, the temporal nature of processing in these processes obviates the application of traditional waste minimization, material recycling, or energy integration schemes. Further, most of the existing methodologies for generating sustainable alternatives are restricted to specific problems, such as reaction byproduct, wastewater, or solvent minimization. In this paper, we propose an intelligent simulation–Optimization Framework for identifying comprehensive sustainable alternatives for batch processes. We differentiate between wastes generated by the reaction–separation process and cleaning wastes. A P-graph-based approach is used for identifying the root cause of process waste generation and generating broad design alternatives. Specific variable-level design solutions are then identified and evaluated using process simulation. The cleaning wastes resulting from the optimized process are also minimized using a source-sink allocation method that allows design of recycle network structure. A multi-objective stochastic Optimization method is used to integrate the analysis so that the overall process economic and environmental footprint is optimized. We illustrate the proposed methodology using a well-known literature case study involving reaction, distillation and washing operation.
Bingni W Brunton - One of the best experts on this subject based on the ideXlab platform.
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numerical differentiation of noisy data a unifying multi objective Optimization Framework
arXiv: Dynamical Systems, 2020Co-Authors: Floris Van Breugel, Nathan J Kutz, Bingni W BruntonAbstract:Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an \textit{ad hoc} process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective Optimization Framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our Framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for automatically selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library \texttt{pynumdiff} to facilitate easy application to diverse datasets (this https URL).
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numerical differentiation of noisy data a unifying multi objective Optimization Framework
IEEE Access, 2020Co-Authors: Floris Van Breugel, Nathan J Kutz, Bingni W BruntonAbstract:Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an ad hoc process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective Optimization Framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our Framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library pynumdiff to facilitate easy application to diverse datasets ( https://github.com/florisvb/PyNumDiff ).
Jan Carmeliet - One of the best experts on this subject based on the ideXlab platform.
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Optimization Framework for distributed energy systems with integrated electrical grid constraints
Applied Energy, 2016Co-Authors: Boran Morvaj, Ralph Evins, Jan CarmelietAbstract:Abstract Distributed energy systems (DES) can help in achieving less carbon-intensive energy systems through efficiency gains over centralized power systems. This paper presents a novel Optimization Framework that combines the optimal design and operation of distributed energy systems with calculations of electrical grid constraints and building energy use. The Framework was used to investigate whether the negative impact of distributed generation on distribution grids can be mitigated and grid upgrades avoided by properly designing and determining operation strategies of DES. Three new methods for integrating grid constraints were developed based on different combinations of a genetic algorithm and a mixed-integer linear programme. A case study is defined in order to analyze the optimality, accuracy of power flow calculation and solving performance of each method. The comparison showed that each method has advantages and disadvantages and should therefore be chosen based on the application and objectives. The results showed that the electrical grid constraints have a significant impact on the optimal solutions, especially at high levels of renewable energy use, highlighting their importance in such Optimization problems. The inclusion of such constraints directly in the operational scheduling achieved an additional 18% reduction in carbon emissions for a given cost compared to checking the validity of solutions a posteriori. Furthermore, by properly designing and determining operation schedules of DES, it is possible to integrated 40% more renewables without grid upgrades.
Alireza Askarzadeh - One of the best experts on this subject based on the ideXlab platform.
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multi objective Optimization Framework of a photovoltaic diesel generator hybrid energy system considering operating reserve
Sustainable Cities and Society, 2018Co-Authors: Zahra Movahediyan, Alireza AskarzadehAbstract:Abstract This paper presents a new multi-objective Optimization Framework to design a photovoltaic/diesel generator (PV/DG) power generation system for an isolated community in the presence of operating reserve. Operating reserve is the safety margin to ensure the system reliability despite variability in load and PV power supply. For this aim, three objectives, namely, total net present cost (TNPC), CO2 emission and loss of power supply probability (LPSP) have been considered to have a cost-effective, clean and reliable power generation system. In order to solve the Optimization problem, multi-objective version of a recently developed meta-heuristic method, crow search algorithm (CSA), has been proposed. To validate the results obtained by multi-objective CSA (MO-CSA), the sizing problem is also solved by multi-objective particle swarm Optimization (MOPSO). Simulated results show that by considering operating reserve, the size (correspondingly the cost) of the system increases significantly. Moreover, in comparison with MOPSO, the Pareto front obtained by MO-CSA is well-distributed and widely spread.
Floris Van Breugel - One of the best experts on this subject based on the ideXlab platform.
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numerical differentiation of noisy data a unifying multi objective Optimization Framework
arXiv: Dynamical Systems, 2020Co-Authors: Floris Van Breugel, Nathan J Kutz, Bingni W BruntonAbstract:Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an \textit{ad hoc} process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective Optimization Framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our Framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for automatically selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library \texttt{pynumdiff} to facilitate easy application to diverse datasets (this https URL).
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numerical differentiation of noisy data a unifying multi objective Optimization Framework
IEEE Access, 2020Co-Authors: Floris Van Breugel, Nathan J Kutz, Bingni W BruntonAbstract:Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an ad hoc process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective Optimization Framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our Framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library pynumdiff to facilitate easy application to diverse datasets ( https://github.com/florisvb/PyNumDiff ).
