METCAL 在 Co3O4 制备工艺设计中的应用

采用 METCAL 软件创建计算模型，对沉淀 - 热分解法制备四氧化三钴材料的工艺进行了模拟，并简要介绍了过程模拟的步骤和思路。通过对工艺流程的模拟计算，得到了直观的物流分配走向、金属平衡和热平衡数据等，为四氧化三钴材料制备工艺工程设计提供了基础数据支撑。     

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.     

Plasmonic Bi nanoparticle decorated BiVO4/rGO as an efficient photoanode for photoelectrochemical water splitting

We report the application of plasmonic Bi nanoparticles supported rGO/BiVO₄ anode for photoelectrochemical (PEC) water splitting. Nearly, 2.5 times higher activity was observed for Bi-rGO/BiVO₄ composite than pristine BiVO₄. Typical results indicated that Bi-rGO/BiVO₄ exhibits the highest current density of 6.05 mA/cm² at 1.23 V, whereas Bi–BiVO₄ showed the current density of only 3.56 mA/cm². This enhancement in PEC activity on introduction of Bi-rGO is due to the surface plasmonic behavior of BiNPs, which improves the absorption of radiation thereby reduces the charge recombination. Further, the composite electrode showed good solar to hydrogen conversion efficiency, appreciable incident photon-to-current efficiency and low charge transfer resistance. Hence, Bi-rGO/BiVO₄ provides an opportunity to realize PEC water splitting.     

A Method for Systematic Assessment of Intrinsically Disordered Protein Regions by NMR

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a “chimeric membrane protein”-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained ¹⁵N-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools.     

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.