Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units

Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.

Optimization and Control of Extractive Distillation with Heat Integration for Separating Benzene/Cyclohexane Mixtures

In this work, the extractive distillation with heat integration process is extended to separate the pressure-insensitive benzene-cyclohexane azeotrope by using furfural as the entrainer. The optimal design of extractive distillation process is established to achieve minimum energy requirement using the multi-objective genetic algorithm, and the results show that energy saving for this heat integration process is 15.7%. Finally, the control design is performed to investigate the system's dynamic performance, and three control structures are studied. The pressure-compensated temperature control scheme is proposed based on the first two control structures, and the dynamic responses reveal that the feed disturbances in both flow rate and benzene composition can be mitigated well.

Integrated Electrode-Electrolyte Optimization to Manufacture a Real-Life Applicable Aqueous Supercapacitor with Record-Breaking Lifespan

Aqueous supercapacitors(SCs)have been regarded as a promising candidate for commercial energy storage device due to their superior safety,low cost,and environmental benignity.Unfortunately,an age-old challenge of achieving both long electrode lifespan and qualified energy-storage property blocks their practical application.Herein,we develop an electrode-electrolyte integrated optimization strategy to fulfill the real-life device requirements.Electrode optimization simultaneously regulates the nanomorphology and surface chemistry of the tungsten oxide anode,resulting in superior electrochemical performance given by an ideal“bird-nest”structure with optimal oxygen vacancy status;the anodes interact with and are protected from dissolution and structural collapse by the rationally designed hybrid electrolyte with optimized pH and facilitated cation desorption behavior.Collaboratively,a record-breaking durability of no capacitive decay after 250000 cycles is achieved.On the basis of this integrated optimization,the first aqueous pouch SCs with real-life practicability were manufactured by a soft-package encapsulation technique,which can steadily power commercial 3 C products such as tablets and smartphones and maintain safely working against extreme conditions.This work demonstrates the possibility of using aqueous energy storage devices with enhanced safety and lower cost to replace the commercial organic counterparts for wide range of daily applications.

Multidisciplinary Design Optimization of A Human Occupied Vehicle Based on Bi-Level Integrated System Collaborative Optimization

The design of Human Occupied Vehicle （HOV） is a typical multidisciplinary problem, but heavily dependent on the experience of naval architects at present engineering design. In order to relieve the experience dependence and improve the design, a new Multidisciplinary Design Optimization （MDO） method ＂Bi-Level Integrated System Collaborative Optimization （BLISCO）＂ is applied to the conceptual design of an HOV, which consists of hull module, resistance module, energy module, structure module, weight module, and the stability module. This design problem is defined by 21 design variables and 23 constraints, and its objective is to maximize the ratio of payload to weight. The results show that the general performance of the HOV can be greatly improved by BLISCO.

Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition

How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.

Integrated Optimization of Structure and Control Parameters for the Height Control System of a Vertical Spindle Cotton Picker

Vertical picking method is a predominate method used to harvest cotton crop.However,a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field.Thus,how to realize a precise height control of the cotton picker is a crucial issue to be solved.The objective of this study is to design a height control system to avoid the collision.To design it,the mathematical models are established first.Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston,response time and displacement error of the height control system as the opti-mization objectives.An integrated optimization approach that combines optimization via simulation,particle swarm optimization and simulated annealing is proposed to solve the model.Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston,but also decrease the response time and displacement error of the height control system.

Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming

Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main categories: optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimi2ation algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functions are designed based on the specific weight coefficient method and ' ideal point strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot,the high-quality solutions are found at a lower cost.

Efficiency analysis of numerical integrations for finite element substructure in real-time hybrid simulation

Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.

Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

The Integrated System of Aerial Photogrammetry and Tower Locations Optimization for Transmission Lines

This paper describes the functions and the features of the integrated system of aerial survey and tower locations optimization for transmission lines, which includes all stages from data acquisition, data transmission and data processing to automatic optimization of the tower locations and drawing. The paper also briefly describes the economic benefit gained from this system, and finally proposes the directions of the future development for this system.

A new approach for flow simulation in complex hydraulic fracture morphology and its application:Fracture connection element method

Efficient flow simulation and optimization methods of hydraulic fracture morphology in unconventional reservoirs are effective ways to enhance oil/gas recovery.Based on the connection element method(CEM)and distribution of stimulated reservoir volume,the complex hydraulic fracture morphology was accurately described using heterogeneous node connection system.Then a new fracture connection element method(FCEM)for fluid flow in stimulated unconventional reservoirs with complex hydraulic fracture morphology was proposed.In the proposed FCEM,the arrangement of dense nodes in the stimulated area and sparse nodes in the unstimulated area ensures the calculation accuracy and efficiency.The key parameter,transmissibility,was also modified according to the strong heterogeneity of stimulated reservoirs.The finite difference and semi-analytical tracking were used to accurately solve the pressure and saturation distribution between nodes.The FCEM is validated by comparing with traditional numerical simulation method,and the results show that the bottom hole pressure simulated by the FCEM is consistent with the results from traditional numerical simulation method,and the matching rate is larger than 95%.The proposed FCEM was also used in the optimization of fracturing parameters by coupling the hydraulic fracture propagation method and intelligent optimization algorithm.The integrated intelligent optimization approach for multi-parameters,such as perforation number,perforation location,and displacement in hydraulic fracturing is proposed.The proposed approach was applied in a shale gas reservoir,and the result shows that the optimized perforation location and morphology distribution are related to the distribution of porosity/permeability.When the perforation location and displacement are optimized with the same fracture number,NPV increases by 70.58%,which greatly improves the economic benefits of unconventional reservoirs.This work provides a new way for flow simulation and optimization of hydraulic fracture morphology of multi-fractured horizontal wells in unconventional reservoirs.

Novel Block Chain Technique for Data Privacy and Access Anonymity in Smart Healthcare

The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applications have beenextensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstaclesfaced by the extensive acceptance and usage of these emerging technologies aresecurity and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, theexisting system has issues with specific security issues, privacy-preserving rate,information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymityin terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user. After that, theGeo-Location and IP Address values collected during registration are convertedinto Hash values using Adler 32 hashing algorithm, and the private and publickeys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server,which redirects the request to the associated IoT device in order to obtain thesensed IoT data. The detected data is anonymized in the device and stored inthe cloud server using the Linear Scaling based Rider Optimization algorithmwith integrated KL Anonymity (LSR-KLA) approach. After that, the Time-stamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanismis used to permit the authorized user to access the data, and the blockchain servertracks the entire transaction. The experimental findings showed that the proposedLSR-KLA and TSPP-SS technique provides better performance in terms of higherprivacy-preserving rate, lower information loss, execution time, and Central Processing Unit (CPU) usage than the existing techniques. Thus, the proposed method allows for better data privacy in the smart healthcare network.

Aerodynamic/control integrated optimization method for unpowered high-speed vehicle configuration design

The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large airspace.The conventional aircraft conceptual design process follows a sequential design approach,and there is an artificial separation between the disciplines of aerodynamics and control,neglecting the coupling effects arising from their interaction.As a result,this design process often requires extensive iterations over long periods when applied to high-speed vehicles,and may not be able to effectively achieve the desired design objectives.To enhance the overall performance and design efficiency of high-speed vehicles,this study integrates the concept of Active Control Technology(ACT)from modern aircraft into the philosophy of aerodynamic/control integrated optimization.Two integrated optimization strategies,with differences in coupling granularity,have been developed.Subsequently,these strategies are put into action on a biconical vehicle that operates at Mach 5.The results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76%and 28.29%respectively compared to the base model under high-speed conditions,demonstrating the feasibility and effectiveness of the method and optimization strategies employed.Moreover,in comparison to the single-stage strategy,the multi-stage strategy takes into deeper consideration the impact of control capacity.As a result,the control performance of the synthesis opti-mal model derived from the multi-stage strategy improves by 13.99%,whereas the single-stage strategy only achieves a 5.79%improvement.This method enables a fruitful interaction between aerodynamic configuration design and control system design,leading to enhanced overall performance and design efficiency.Furthermore,it improves the controllability of high-speed vehicles,mitigating the risk of mission failure resulting from an ineffective control system.

Cross-level steam load smoothing and optimization in industrial parks using data-driven approaches

This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.

APPLICATION OF HYBRID AERO-ENGINE MODEL FOR INTEGRATED FLIGHT/PROPULSION OPTIMAL CONTROL

The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.

Primary studies on integration optimization of differential steering of electric vehicle with motorized wheels based on quality engineering

The model of the differential steering system(DSS) of electric vehicle with motorized wheels and the three-degree-of-freedom dynamic model of vehicle are built.Based on these models,the concepts and quantitative expressions of steering road feel,steering portability and steering stability are proposed.Through integrating the Monte Carlo descriptive sampling,elitist non-dominated sorting genetic algorithm(NSGA-II) and Taguchi robust design method,the system parameters are optimized with steering road feel and steering portability as optimization targets,and steering stability and steering portability as constraints.The simulation results show that the system optimized based on quality engineering can improve the steering road feel,guarantee steering stability and steering portability and thus provide a theoretical basis for the design and optimization of the electric vehicle with motorized wheels system.

An Innovative Bias-Correction Approach to CMA-GD Hourly Quantitative Precipitation Forecasts

This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration(CMA).Three techniques are used to generate multi-method calibrated members for OPI:deep neural network(DNN),frequency-matching(FM),and optimal threat score(OTS).The results are as follows:(1)The QPF using DNN follows the basic physical patterns of CMA-GD.Despite providing superior improvements for clear-rainy and weak precipitation,DNN cannot improve the predictions for severe precipitation,while OTS can significantly strengthen these predictions.As a result,DNN and OTS are the optimal members to be incorporated into OPI.(2)Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation.Compared with the CMA-GD,OPI improves the TS by 2.5%,5.4%,7.8%,8.3%,and 6.1%for QPFs from clear-rainy to rainstorms in the verification dataset.Moreover,OPI shows good stability in the test dataset.(3)It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation.Therefore,improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability.

Review:Research Progress for Electric Vehicle Hub Motor Driving Technology

Due to high efficiency,high controllability,high integration,lightweight,and other advantages,electric vehicle with hub motor driving technology has become an emerging trend of chassis technology.This paper concludes the current state⁃of⁃the⁃art of hub motor drive technologies.Firstly,it summarizes recent hub motor drive products and makes suggestions for hub motor drive schemes in different application scenarios.Then research on hub motor drive key technologies such as integrated design,thermal optimization,lightweight,and intensity optimization is investigated.Considering the high response accuracy and zero delay characteristic of hub motor driving system combined with advanced distributed dynamics control technology that can further improve vehicle performance,this paper also analyzes existing chassis dynamics control technologies of hub motor driving system.Considering the development trend of vehicle electrification,intelligentization,network connection,and current research,this paper makes some forecasts for hub motor drive technologies development in the conclusion.

Performance analysis of semi-active cab’s hydraulic system of the vibratory roller using optimal fuzzy-PID control

In order to evaluate the performance of semi-active cab’s hydraulic mounts(SHM)of the off-road vibratory roller with the optimal fuzzy-PID(proportional integral derivative)control,a nonlinear dynamic model of the vehicle interacting with off-road terrains is established based on Matlab/Simulink software.The weighted root-mean-square(RMS)acceleration responses of the driver’s seat heave and the cab’s pitch angle are chosen as objective functions.The SHM is then optimized and analyzed via the optimal fuzzy-PID control under different operation conditions.The simulations results show that the driver’s ride comfort and the cab shaking are greatly affected by the off-road terrains under various operating conditions of the vehicle,especially at the speed from 8 to 12 km/h on a very poor terrain surface of Grenville soil ground under the vehicle travelling.With SHM using the optimal fuzzy-PID control,the driver’s ride comfort and the cab shaking are clearly improved under various operation conditions of the vehicle,particularly at the speed from 6 to 7 km/h of the vehicle traveling.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.