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1.
This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.  相似文献   
2.
Suzuki-Miyaura (S-M) is regarded the most powerful way for synthesis biaryls, triaryls, or incorporating of substituted aryl moieties in organic preparation by the cross-coupling of aryl boronic acid with aryl halides using the Pd catalyst. This work reports the combining of the hydrothermal and microwave-assisted protocol to convert the glucose to magnetic carbon spheres (Fe3O4-CSPs) decorated with Pd nanoparticles (NPs) as the catalyst for Suzuki-Miyaura cross-coupling reactions. The physicochemical properties in the produced composite were examined using FESEM, HRTEM, nitrogen isotherms, Raman spectroscopy, FTIR, XPS, and XRD. The as-fabricated composite Pd/Fe3O4-CSPs is mostly spherical with a core–shell structure and possesses a great surface area of 253.2 m2·g-1. Its catalytic performance demonstrates that the composite has excellent stability and high tolerance Suzuki-Miyaura cross-coupling reactions in 30 min at 80 ℃. Both activated and deactivated aryl halides provided excellent yield. The as-fabricated catalyst was recycled for up to four catalytic cycles without a substantial decline in performance. Moreover, this research offers a facile roadmap for synthesizing Pd/Fe3O4-CSPs composites and promoting the practical implementation of Pd/Fe3O4-CSPs catalysts for organic transformation processes.  相似文献   
3.
Journal of Porous Materials - The transformation of light naphtha to value-added aromatic compounds is gaining momentum in the petrochemical industry. In this work, a series of metal modified...  相似文献   
4.
This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm3 of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.  相似文献   
5.
The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.  相似文献   
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7.
World Wide Web - The integration of Cloud computing and Internet of Things led to rapid growth in the edge computing field. This would not be achievable without combining the data centers’...  相似文献   
8.
Silicon - The silica-ortho-phenylenediamine(RHAOPDA) derived from Rice Husk Ash and o- phenylenediamine (OPDA) were synthesized. The prepared material was characterized by different techniques; N2-...  相似文献   
9.
One of the methods of pollution separating from urban and industrial wastewater is the use of composite membranes based on polymer. The modified composite  相似文献   
10.
Slewa  Lary H.  Abbas  Tariq A.  Ahmed  Naser M.  Hassan  Z. 《SILICON》2020,12(7):1761-1768
Silicon - Porous silicon (PSi) samples were prepared via electroless stain etching of silicon substrates in HF using V2O5 as an oxidant and ethanol as a surfactant for different etching times. The...  相似文献   
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