Optimal control of combined heat and power units under varying thermal loads

This paper describes a model based optimizer that allows a combined heat and power (CHP) unit to supply backup power to a Smart Grid on the one hand and minimize the cost for heat and power supply on the other hand. The model of the CHP unit is lean but nevertheless accurately represents the unit behavior, including thermal behavior of the storage as well as the aging effect of engine starts. Thanks to the small model dimension we can solve the optimal dispatch problem efficiently using dynamic programming. Two selected soft- and hard-ware in the loop tests are discussed to demonstrate the performance of the approach. A re-optimization strategy is discussed that allows reactions to wrong predictions of external influences like weather.     

A novel evolutionary-negative correlated mixture of experts model in tourism demand estimation

Mixtures of experts (ME) model are widely used in many different areas as a recognized ensemble learning approach to account for nonlinearities and other complexities in the data, such as time series estimation. With the aim of developing an accurate tourism demand time series estimation model, a mixture of experts model called LSPME (Lag Space Projected ME) is presented by combining ideas from subspace projection methods and negative correlation learning (NCL). The LSPME uses a new cluster-based lag space projection (CLSP) method to automatically obtain input space to train each expert focused on the difficult instances at each step of the boosting approach. For training experts of the LSPME, a new NCL algorithm called Sequential Evolutionary NCL algorithm (SENCL) is proposed that uses a moving average for the correlation penalty term in the error function of each expert to measure the error correlation between it and its previous experts. The LSPME model was compared with other ensemble models using monthly tourist arrivals to Japan from four markets: The United States, United Kingdom, Hong Kong and Taiwan. The experimental results show that the estimation accuracy of the proposed LSPME model is significantly better than the other ensemble models and can be considered to be a promising alternative for time series estimation problems.     

A numerical approach for a comparative study of laser drilling process under single and repetitive pulse

An axisymmetric model is developed to study laser drilling process under a single pulse as well as repetitive laser pulse. The laser pulse irradiated on the surface of the workpiece is volumetric and Gaussian in nature. The laser irradiated surface is subjected to convective‐radiative boundary condition while rest of the surfaces are insulated. Finite volume method is used to discretize the domain under consideration. The resulting algebraic equations are solved with the help of the tridiagonal matrix algorithm to find temperature distribution throughout the domain. The enthalpy‐porosity method is used to track the solid‐liquid interface generated during the laser melting process. Convective heat transfer occurs inside the generated melt pool. The current model is first used to validate the results of the existing literature and as the results agreed well, further studies are made to find out the advantages of using repetitive laser pulse over single pulse laser source for laser drilling process for the same laser energy and total heating duration. Vaporization has been avoided during the process and metal removal occurs through melting only. The present numerical model can provide some insight for practical laser drilling process.     

Model assessment of MPCs with control ranges: An industrial application in a delayed coking unit

Poor model quality is one of the most frequent causes of performance deterioration in Model Predictive Controllers. As such, frequent model evaluation and correction is fundamental. Some assessment methods are reported in the literature, but most cannot deal with Model Predictive Controllers (MPCs) without fixed setpoints for controlled variables. Botelho et al. (2015, 2016a, 2016b) proposed a series of methods that include the controller tuning and the applied MPC implementation in the assessment procedure. Their main advantage is setpoint independence. This paper analyzes the application of these methods in an industrial MPC with control ranges. The system studied is an MPC of a fractionating column in a delayed coker unit of a refinery in Brazil. The results demonstrate that the method is capable of correctly quantifying the effect of modeling problems and identifying whether they are related to a model-plant mismatch or unmeasured disturbance.     

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research,a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation.The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs.In this procedure,the Artificial Neural Network was trained,tested and validated using assay values obtained from exploratory drillholes.Next,the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively.Finally,the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model.The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades.The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement.It could also be an effective mineral reserve evaluation method that could produce optimum block model for mine design.     

基于双树复小波的无参考立体图像质量评价

随着3D技术的不断发展，立体图像的使用领域越来越广泛，同时人们对图像的清晰度要求越来越高，因此，立体图像的质量评价成为关注点，基于此，提出了一种基于双树复小波变换的立体图像质量评价算法。使用双树复小波变换对立体图像的左、右视图进行处理，生成纹理结构图像，且根据最小能量误差的原理，获取左右视图的视差图；对纹理结构图像和视差图提取非对称广义高斯分布模型的参数、梯度幅值、相对梯度方向方差和奇异值曲线与坐标轴的面积等特征；使用AdaBoosting BP神经网络，进行训练和预测立体图像的质量得分。在LIVE立体图像数据库上的实验结果表明，新方法预测得分与主观得分有较好的一致性，获得了比较好的实验结果。     

Assessing plant design with regard to MPC performance

Model Predictive Control is ubiquitous in the chemical industry and offers great advantages over traditional controllers. Notwithstanding, new plants are being projected without taking into account how design choices affect the MPC’s ability to deliver better control and optimization. Thus a methodology to determine if a certain design option favours or hinders MPC performance would be desirable. This paper presents the economic MPC optimization index whose intended use is to provide a procedure to compare different designs for a given process, assessing how well they can be controlled and optimised by a zone constrained MPC. The index quantifies the economic benefits available and how well the plant performs under MPC control given the plant’s controllability properties, requirements and restrictions. The index provides a monetization measure of expected control performance.This approach assumes the availability of a linear state-space model valid within the control zone defined by the upper and lower bounds of each controlled and manipulated variable. We have used a model derived from simulation step tests as a practical way to use the method. The impact of model uncertainty on the methodology is discussed. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of a MPC to reach dynamic equilibrium near process restrictions, which in turn increases product quality giveaway and costs. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the applicability of the index.     

Extended Process to Product Modeling (xPPM) for integrated and seamless IDM and MVD development

This paper proposes a new extended Process to Product Modeling (xPPM) method for integrated and seamless information delivery manual (IDM) and model view definition (MVD) development. Current IDM development typically uses Business Process Modeling Notation (BPMN) to represent a process map (PM). Exchange requirements (ERs) and functional parts (FPs) specify the information required when information is exchanged between different activities. A set of information requirements, specifically defined as a subset of Industry Foundation Classes (IFC), is called an MVD. Currently however, PMs, ERs, FPs, and MVDs are developed as separate documents through independent development steps. Moreover, even though ERs and FPs are designed to be reused, tracking and reusing the ERs and FPs developed by others is practically impossible. The xPPM method is proposed to provide a tight connection between PMs, ERs, FPs, and MVDs and to improve the reusability of predefined ERs and FPs. The theoretical framework is based on the approach of the Georgia Tech Process to Product Modeling (GTPPM) to suit the IDM development process. An xPPM tool is developed, and the validity of xPPM is analyzed through the reproduction of existing IDMs and MVDs. The benefits and limitations of xPPM and lessons from the applicability tests are discussed.     

Reduced order finite element model for elastohydrodynamic lubrication: Circular contacts

This paper presents an extension of the reduced order finite element model to the case of circular elastohydrodynamic lubricated (EHL) contacts under isothermal Newtonian considerations. The line contact model was developed and validated in a previous work (Advances in Engineering Software, 2013; 56:51−62). The model is based on a finite element discretization of the EHL equations: Reynolds, linear elasticity and load balance with a reduced order model for the linear elasticity part. All equations are solved simultaneously in a fully-coupled framework using a damped-Newton procedure allowing fast convergence rates for the global solution. This model combines fast convergence rates, reduced memory requirements and negligible model reduction errors compared to the full model which makes it an attractive tool for EHL contact performance prediction.     

Computing Maximum Likelihood Estimates for the Mixed A.O.V. Model Using the W Transformation

The W transformation, a matrix transformation, is developed and applied for the mixed analysis of variance model to compute maximum likelihood estimates of the variance components and fixed parameters. This transformation not only eliminates the need for the explicit computation of the n × n inverse matrix H^?l but permits handling the iterative calculations such that they do not depend upon n (the number of observations) in any way. Although not wedded to a particular numerical method, the W transformation is implemented in conjunction with a modified Newton-Raphson method in which variance components are restricted to being non-negative.