Shanghai Central Tower, as China’s landmark super high-rise building, has attracted wide attention since its construction. Because it is much higher than ordinary buildings, it also poses a challenge to construction ...
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Anaerobic Digestion (AD) technology, known for its waste recycling and environmental clean-up capabilities, also generates renewable energy in the form of biogas. The need to reduce dependence on fossil fuels has exte...
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ISBN:
(数字)9798350353754
ISBN:
(纸本)9798350353761
Anaerobic Digestion (AD) technology, known for its waste recycling and environmental clean-up capabilities, also generates renewable energy in the form of biogas. The need to reduce dependence on fossil fuels has extensively stimulated interest in this process. The process is known for its complexity, high sensitivity, and potential for instability, which is inherently reflected in the models describing it. Models contain uncertain parameters and highly sensitive to process noise. Parameter identification plays a crucial role during modeling and is a delicate task preceding control law development. It is mandatory to have the best possible estimates of a model that guarantees efficient predictions. This paper focuses on the optimization of stoichiometric and kinetic coefficients of the Acidogenesis Methanogenesis 2 model (AM2). We present in this study the use of the Whale Optimization Algorithm (WOA) to optimize AM2 model parameters and validate the results using data acquired from a Anaerobic Digestion Model $\mathbf{N}^{\circ} 01$ (ADM1). The identification algorithm was implemented in MATLAB 2018. To validate the effectiveness of our proposed approach, we conduct a comparative analysis with Genetic Algorithm (GA). The results, despite certain limitations, underscore the robustness of WOA for parameter estimation.
Power quality upgrade in grid-connected photovoltaic systems ensures stable and highly efficient operation of modern energy grids. Meanwhile, the high penetration of renewable energy sources brings real challenges to ...
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In this paper, the vortex cavitation and throttling characteristics of the tunable cavitation throttling device are analyzed by combining the Schnerr-Sauer and Zwart-Gerbe-Belamri cavitation models for large eddy simu...
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At present, cyber-physical systems are widely used in avionics, telemedicine, automotive electronics and other fields, and the software and hardware subsystems in cyber-physical systems are closely coupled with each o...
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The time-varying and multi-dimensional characteristics are major causes of the low performance of soft sensors in chemical processes. To solve the problem, an improved adaptive soft sensor modeling method is proposed....
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The time-varying and multi-dimensional characteristics are major causes of the low performance of soft sensors in chemical processes. To solve the problem, an improved adaptive soft sensor modeling method is proposed. This method obtains predicted deviation by modular steps of moving window and evaluates deterioration of soft sensors via ttest adaptively. Besides, this paper combines the moving window-autoassociative neural network (AANN) method to update both the modeling auxiliary variable and the auxiliary variable data. data simulation and result analysis obtained via a continuous stirred tank reactor (CSTR) and a debutanizer column process (DCP) show that the improved adaptive soft sensor modeling method proposed in this paper can evaluate the deterioration of soft sensors and update the soft sensor model adaptively, and improve the predicted performance of soft sensors for time-varying and multi-dimensional chemical processes.
In the field of education, there is a gap between research and practice. Lack of data standardization and collection inhibits comparability and generalizability of findings in the context of population heterogeneity. ...
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The full characteristic model of hydraulic turbine must be considered in the research of control and transition process calculation of hydraulic turbine generator unit. In order to obtain the full characteristic model...
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Frequent subgraph mining is a fundamental task in the analysis of collections of graphs. While several exact approaches have been proposed, it remains computationally challenging on large graph datasets due to its inh...
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The proceedings contain 53 papers. The topics discussed include: the development of a game for cognitive remediation therapy (CRT) to improve attention span and memory among children with learning disabilities;managem...
ISBN:
(纸本)9781665476928
The proceedings contain 53 papers. The topics discussed include: the development of a game for cognitive remediation therapy (CRT) to improve attention span and memory among children with learning disabilities;management of raw material needs and safety stock based on data forecast and system dynamics modeling;understanding user behavior with web session clustering and user engagement metrics;dragonfly algorithm strategy parameters analysis on swarm robot multi-target search efficiency;color-assisted multi-input convolutional neural network for cancer classification on mammogram images;simulation program for modeling temperature distribution in a food dehydrator;statistical assessment for point cloud dataset;investigation of learning rate for directed acyclic graph network performance on dysgraphia handwriting classification;utilization of augmented reality in assisting surgical needle insertion guidance;production and capacity planning as well as inventory and distribution control in snack packaging companies using open source ERP simulation;and parameter-replacement functions for stability-guaranteed variable digital filters.
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