Experiments and model development of p-nitrochlorobenzene and naphthalene purification in a continuous tower melting crystallizer

With the increasing demand for high-purity products,the industrial application of melt crystallization technology has been highly concerned.In this study,the purification process of nitrochlorobenzene binary eutectic system(NBES)and naphthalene–benzothiophene solid solution system(NBSSS)in tower melting crystallizer is analyzed,and a mathematical model of crystallization process is established.The key parameters in terms of feed concentration,crystal bed height,reflux ratio and stirring speed effi-ciency on purification effects were discussed by the established model.The results show that the concentration of p-nitrochlorobenzene was purified from 90.85%to 99.99%,when the crystal bed height is 600 mm,the reflux ratio is 2.5,and the stirring speed is 12 rmin^(-1).The naphthalene concentration is purified from 95.89%to 99.99%,when the crystal bed height is 400 mm,the reflux ratio is 1.43,and the stirring speed is 16 rmin^(-1).The quality of the model is evaluated by the ARD(average relative deviation).The minimum ARD values of the NBES and NBSSS are 2.39%and 5.22%,respectively,indicating the model satisfactorily explains the purification process.

Efficient separation of phenols from coal tar with aqueous solution of amines by liquid-liquid extraction

In this paper,a method of extracting phenols from coal tar by amines aqueous solution was proposed.The effects of various amines on the extraction properties of phenols in coal tar were researched from the views of molecular structure.The parameters such as molar ratio,concentration,extraction time and temperature for the extraction of coal tar by the monoethanolamine and ethylenediamine aqueous solution were examined.The results show that the organic amine with more amino groups,hydroxyl structure and strong electronegativity exhibited better extraction performance.Under the optimal conditions,the extraction yields of phenols in coal tar by the monoethanolamine or ethylenediamine aqueous solution are above 80%,and the recovery yields of amines reach 99%.Furthermore,the probable geometries of complexes formed by the combination of phenols and organic amines were calculated by density function theory.In addition,several thermodynamic models were evaluated through comparing the relative deviation of simulation results by ASPEN PLUS to the experimental ones,which provide feasibility thermodynamic models for the simulation of extraction process.The present work affords a mild,efficient and green approach for the extraction of phenols from coal tar by an aqueous solution of amines in industry application.

Small-signal stability analysis of photovoltaic generation connected to weak AC grid

A small-signal model of photovoltaic(PV)generation connected to weak AC grid is established based on a detailed model of the structure and connection of a PV generation system. An eigenvalue analysis is then employed to study the stability of PV generation for different grid strengths and control parameters in a phaselocked loop(PLL) controller in the voltage source converter. The transfer function of the power control loop in the dq rotation frame is developed to reveal the influence mechanism of PLL gains on the small-signal stability of PV generation. The results can be summarized as follows:(1)oscillation phenomena at a frequency of about 5 Hz may occur when the grid strength is low;(2) the tuning control parameters of the PLL have a noticeable effect on the damping characteristics of the system, and larger proportional gain can improve the system damping;(3)within a frequency range of 4-5 Hz,the PLL controller has positive feedback on the power loop of PV generation. A virtual inductance control strategy is proposed to improve the operational stability of PV generation. Finally, a simulation model of PV generation connected to weak AC grid is built in PSCAD/EMTDC and the simulation results are used to validate the analysis.

CEPC Technical Design Report

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international partners.The complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs factory.In its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs bosons.Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of magnitude.This Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering aspects.Additionally,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection procedures.Construction is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 years.The commencement of experiments and data collection could potentially be initiated in the mid-2030s.

Implementation for a cloud battery management system based on the CHAIN framework

An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long lifetimes.With recent developments in cloud computing and the proliferation of big data,machine learning approaches have begun to deliver invaluable insights,which drives adaptive control of battery management systems(BMS)with improved performance.In this paper,a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed,with the composition and function of each link described.Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network(CHAIN)framework to provide multi-scale insights,more advanced and efficient algorithms can be used to realize the state-of-X es-timation,thermal management,cell balancing,fault diagnosis and other functions of traditional BMS system.The battery intelligent monitoring and management platform can visually present battery performance,store working-data to help in-depth understanding of the microscopic evolutionary law,and provide support for the development of control strategies.Currently,the cloud-based BMS requires more effects on the multi-scale inte-grated modeling methods and remote upgrading capability of the controller,these two aspects are very important for the precise management and online upgrade of the system.The utility of this approach is highlighted not only for automotive applications,but for any battery energy storage system,providing a holistic framework for future intelligent and connected battery management.