EBHP分解飞温模型建立及控制分析

对乙苯过氧化氢(EBHP)分解构建了飞温模型,绘制有冷却和无冷却体系两种工况的飞温曲线,分析了乙苯注入、紧急冷却水通入措施对飞温的抑制作用。工艺模拟试验结果表明:该EBHP飞温模型能较好预测温度随时间的变化情况,认定120℃为系统报警温度值,140℃为系统联锁值。以某63/30万t/a的苯乙烯/环氧丙烷(SMPO)装置为例,假定EBHP进料量为1000 t/h,用EBHP飞温模型核算,乙苯注入量(w)为30%的进料量时、或者紧急冷却水通入量(w)为进料量的6.5%时,系统均能快速终止飞温,且效果明显。这一研究结果为装置的安全设计和稳定运行提供了依据。     

磷酸高效分解磷矿实验研究

为改进湿法磷酸生产工艺,提高副产石膏的品质,减少湿法磷酸固体副产物磷石膏堆存产生的经济和环保压力,进行了磷酸分解磷矿制磷酸的实验研究。采用贵州某磷矿为主要原料,研究了湿法磷酸预分解磷矿的反应条件。通过单因素实验确定了湿法磷酸分解磷矿最优反应条件：反应温度为75 ℃,磷酸与磷矿的质量比（液固比）为9,反应时间为3 h,磷酸质量分数为30%（以五氧化二磷计）。在此条件下,磷矿的分解率为98.62%。     

Optimal Pricing Algorithm in the Electricity Market with Battery and Accumulator and Demand–Supply Balancing

This paper considers the day‐ahead market with batteries and accumulators to level power generation. First, we model consumers with batteries, generators with batteries, and accumulators, all of whom strive to maximize their own profit. An optimal pricing algorithm based on dual decomposition and the steepest descent method is derived and shown to be stable. Finally, the effectiveness of the algorithm is demonstrated by means of numerical simulations which indicate that it achieves demand–supply balance.     

Spectral Animation Compression

This paper presents a spectral approach to compress dynamic animation consisting of a sequence of homeomor-phic manifold meshes. Our new approach directly compresses the field of deformation gradient d...     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

Trajectory-Shaping Guidance with final speed and load factor constraints

This paper describes the design of a guidance law used for guiding a hypersonic gliding vehicle against a ground target from a near-vertical orientation with a specified final speed and a near-zero final load factor. The guidance law consists of two terms: one is Trajectory-Shaping Guidance (TSG) used for steering the vehicle to the target from the specified orientation; the other is Final-Speed-Control Scheme (FSCS) used for controlling the vehicle to perform lateral maneuver to adjust the final speed. Further, the generalized closed form solutions of TSG are obtained from a more general linearized engagement model, where the speed of the vehicle can be an arbitrary positive function of time. By analyzing these solutions, the stability domain of the guidance coefficients is obtained such that the final load factor is zero. This domain is not affected by the change rate of the speed. Thus, according to this analysis, the proposed guidance law can achieve a near zero final load factor by properly selecting the guidance coefficients in the stability domain.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

The Investigation of the Fractional-View Dynamics of Helmholtz Equations Within Caputo Operator

It is eminent that partial differential equations are extensively meaningful in physics, mathematics and engineering. Natural phenomena are formulated with partial differential equations and are solved analytically or numerically to interrogate the system’s dynamical behavior. In the present research, mathematical modeling is extended and the modeling solutions Helmholtz equations are discussed in the fractional view of derivatives. First, the Helmholtz equations are presented in Caputo’s fractional derivative. Then Natural transformation, along with the decomposition method, is used to attain the series form solutions of the suggested problems. For justification of the proposed technique, it is applied to several numerical examples. The graphical representation of the solutions shows that the suggested technique is an accurate and effective technique with a high convergence rate than other methods. The less calculation and higher rate of convergence have confirmed the present technique’s reliability and applicability to solve partial differential equations and their systems in a fractional framework.