塔河东河塘组测井精细解释模型研究

位于塔河地区古生界的东河塘组具有一定的油气勘探前景,但其试油结果却不理想,在此情况下,我们以该地层的岩心分析资料、录井、测井资料为依据,在分析了上述地层的岩性、物性特征的基础上,开展其四性关系的研究。并建立了泥质含量计算模型,孔隙度解释模型,渗透率模型以及含水饱和度计算模型。建立的解释模型具有较高的精度,与现场实际情况吻合,对提高该区油藏储层测井精细解释的准确性具有实际意义。     

核磁共振束缚水饱和度模型研究

岩心最佳饱和度模型的确定是核磁共振测井实验研究工作的重要方面,有助于现场测井中有关参数的合理选择,用以更好地评价油、气储层。分析岩心核磁共振实验资料,采用频谱法（SBVI）计算束缚水饱和度模型参数,选定适合区块的模型参数。     

有毒介质泄漏扩散模型在尿素装置安全评价中的应用

总结归纳了有毒介质泄漏扩散过程特点及泄露量计算的模型。以乌鲁木齐石化尿素生产装置为例进行有毒介质泄露扩散分析,在此基础上运用计算机模拟软件,模拟有毒介质扩散的过程,给出了定量化的评价结果。依据此例,将有毒介质泄露扩散模型引入安全评价的体系中,丰富了安全评价的体系内容,增加了安全评价定量化评价的方式。     

太阳日散射概率密度模型

太阳散射是太阳能系统分析的重要参数。在有太阳总射而没有散射的情况下，散射可通过总射估计出。在北京和成都实测数据基础上，建立了具有2个参数的日散射概率密度模型。该模型的参数通过迭代方法给出。模型基本反映了日散射的分布特征。给出了模型应用的一般方法。讨论了模型的通用性。     

循环双流化床锅炉平衡通风风烟系统建模与仿真

建立了循环双流化床锅炉平衡通风风烟系统的数学模型,并对风烟系统的动态特性作了仿真研究。     

Model simplification and optimization of a passive wind turbine generator

In this paper, the design of a “low cost full passive structure” of wind turbine system without active electronic part (power and control) is investigated. The efficiency of such device can be obtained only if the design parameters are mutually adapted through an optimization design approach. For this purpose, sizing and simulating models are developed to characterize the behavior and the efficiency of the wind turbine system. A model simplification approach is presented, allowing the reduction of computational times and the investigation of multiple Pareto-optimal solutions with a multiobjective genetic algorithm. Results show that the optimized wind turbine configurations are capable of matching very closely the behavior of active wind turbine systems which operate at optimal wind powers by using a MPPT control device.     

Response surface methodology with prediction uncertainty: A multi-objective optimisation approach

In the field of response surface methodology (RSM), the prediction uncertainty of the empirical model needs to be considered for effective process optimisation. Current methods combine the prediction mean and uncertainty through certain weighting strategies, either explicitly or implicitly, to form a single objective function for optimisation. This paper proposes to address this problem under the multi-objective optimisation framework. Overall, the method iterates through initial experimental design, empirical modelling and model-based optimisation to allocate promising experiments for the next iteration. Specifically, the Gaussian process regression is adopted as the empirical model due to its demonstrated prediction accuracy and reliable quantification of prediction uncertainty in the literature. The non-dominated sorting genetic algorithm II (NSGA-II) is used to search for Pareto points that are further clustered to give experimental points to be conducted in the next iteration. The application study, on the optimisation of a catalytic epoxidation process, demonstrates that the proposed method is a powerful tool to aid the development of chemical and potentially other processes.     

Simulation of population balance model-based particulate processes via parallel and distributed computing

Population Balance Models (PBMs), a class of integro partial differential equations, are utilized for simulating dynamics of numerous particulate systems. PBMs describe the time evolutions and distributions of many particulate processes and their efficient and quick simulation are critical for enhanced process control and optimization, especially for real-time applications. However, their intensive computational resource requirement is largely a prohibitive factor in the utility of PBMs for control and optimization. This paper describes how distributed computing systems may be leveraged to execute PBM-based simulations thus achieving time savings, using MATLAB's Distributed Computing Toolbox. A parallel computing algorithm was developed for a three dimensional and four dimensional population balance model with built-in constructs such as SPMD that ran efficiently on a cluster of two quad-core machines linked via a gigabit ethernet cable. Speedup of 6.2 and 5.7 times were achieved with 8 workers, over an un-parallelized code, for a 3 and 4 dimensional PBM respectively. Evaluations on work efficiency and scalability further affirm the algorithms’ respectable performance on larger clusters despite significant memory transfer overheads.     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

Multivariable identification of membrane fouling based on compacted cascade neural network

The membrane fouling phenomenon, reflected with various fouling characterization in the membrane bioreactor(MBR) process, is so complicated to distinguish. This paper proposes a multivariable identification model(MIM) based on a compacted cascade neural network to identify membrane fouling accurately. Firstly, a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network, which could avoid the interference of the overlap inputs. Secondly, a...