大型矿山生产系统的模糊线性规划

针对非确定性环境中大型矿山生产系统规划管理问题,采用模糊数学理论模拟分析非确定性矿山生产规划问题,给出了在多种配置、多种产品、多水平层次、多循环周期物流网络中的模糊线性规划(FLP)模型。通过工程实例计算分析表明,采用模糊线性规划方法确定出的矿山生产规划方案符合工程实际。     

补偿模糊神经网络模型在储层产能预测中的应用

利用MATLAB的神经网络工具箱,把补偿模糊神经网络引入到测井产能预测中,并用已知井段测井数据进行训练学习,来预测同一地区的其他井段的产能。实践结果表明,该方法效果明显,优势突出。     

线性规划实现动态优化的模型预测控制策略

为减小模型预测控制算法中动态优化部分的计算复杂度,提出了用线性规划而非二次规划解决模型预测控制动态优化方法。对单输入单输出和多输入多输出模型预测控制的情形,以控制增量、输出增量和偏移变量作为优化变量,建立线性等式约束和不等式约束,并引入线性目标函数,形成线性规划问题。通过加入多种软约束,可改善动态过程的性能指标,达到平稳控制的目的。最后通过一个实例验证了方法的有效性。     

常规测井资料进行产能预测

油气储层产能评价与预测对于油气田的勘探开发有着重要的意义。它既是提高勘探效果的关键环节之一,又可以为开发的部署与规划提供重要的基础数据。本文旨意在分析常规测井对产预测的基础上,让研究人员可以分析各种公式找出最适合本地区的方法。     

线性规划在矿山生产规划中的应用

结合石人沟铁矿露天转地下开采过渡期产量接替较困难的现状,通过建立采掘计划的线型规划数学模型,得出系统综合优化的采掘方案,从而提高了矿山过渡期开采总体经济效益.     

基于模糊插值模型的短期负荷预测

针对隶属函数对模糊推理模型描述性能和精度的影响,为了进一步提高模糊插值模型的泛化能力,提出了一类参数可调隶属函数,调整其参数从而可改变函数的形状,使之能逼近常用的三角形、高斯型等隶属函数。用它作为插值函数,提出了基于参数可调隶属函数的模糊插值模型,利用粒子群优化算法（PSO）优化模型中参数,并将该模型应用于电力系统的短期负荷预测中,仿真结果证明了该模型的有效性。     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network,AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

油井产能预测评价新方法

评价油井产能对生产开发具有重要指导意义,而针对于非均质性极强缝洞型碳酸盐岩储层,常规产能评价方法有着诸多的不适应性。表现在井底流压测试困难,地层压力衰减较快,求取的采油指数也仅反映测试时产能特征。为更有效的对油井产能动态特征进行分析评价,通过油压及日产量构建产能分析图版,分析油井产能递减率,根据油藏驱动能量特征,将油藏区分为水压驱动油藏和弹性驱动油藏。并利用油压与日产量回归得到直线投影到横坐标得到的产能面积,与单井自喷期实际的日产油能力进行统计回归,得到产能面积与实际日产油能力的相关经验公式,即产能评价预测经验公式。依据此公式只需要判别油藏驱动类型,利用油压及日产量数据计算产能面积,即可求得自喷期单井日产油能力。该方法简单实用,可有效的对本区块油井自喷期日产能力进行评价预测。无需任何测试费用,同时也为类似油田油井产能评价分析提供一种技术思路。     

煤层气井产能预测研究

选取G煤层气田作为研究对象,利用先进的数学建模方法结合生产实际建立相关煤层气产能预测数学模型,本模型相对之前研究成果更加贴近实际生产,考虑到井内动液面、岩石孔隙率等因素,预测结果更加准确,在实际工程中具有指导意义。     

销售计划的线性规划模型

本文建立了一个适应于化肥生产连续而销售呈现季节性的产品销售计划的线性规划模型,提出了求解该模型的方法,并指出成功地应用该模型制订销售计划的关键是价格预测和储存费用的测算。同时提出了停车检修时间安排的原则。     

线性规划配采模型在石灰石矿的应用

运用线性规划数学模型,解决了唐山冀东水泥股份有限公司石灰石矿各采场及矿层间的配采问题.     

Analysis of linear programming in model predictive control

The linear programming formulations of model predictive control are known to exhibit degenerate solution behavior. In this work, a multi-parametric linear programming technique is utilized to analyze the control laws that are generated from various linear programming based MPC routines. These various routines explore a number of factors, including objective function selection and constraint handling on the control laws generated from LP based MPC. A single input single output system is used to demonstrate that the use of input velocity penalties, input blocking, and ∞-norm objective functions can limit or eliminate this undesirable behavior. Finally, a paper machine cross directional control problem is used to demonstrate the control laws generated from LP based MPC for a multivariable example.     

最优控制方法在矿山建设规划中的应用

根据最优控制理论中的最小值原理，建立矿山长远建设规划最优控制离散模型，并以梯度法求解．     

A dynamic programming based approach for explicit model predictive control of hybrid systems

This work presents an algorithm for explicit model predictive control of hybrid systems based on recent developments in constrained dynamic programming and multi-parametric programming. By using the proposed approach, suitable for problems with linear cost function, the original model predictive control formulation is disassembled into a set of smaller problems, which can be efficiently solved using multi-parametric mixed-integer programming algorithms. It is also shown how the methodology is applied in the context of explicit robust model predictive control of hybrid systems, where model uncertainty is taken into account. The proposed developments are demonstrated through a numerical example where the methodology is applied to the optimal control of a piece-wise affine system with linear cost function.     

Modeling and predictive control using fuzzy logic: Application for a polymerization system

In this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming

Hybrid systems are dynamical systems characterized by the simultaneous presence of discrete and continuous variables. Model‐based control of such systems is computationally demanding. To this effect, explicit controllers which provide control inputs as a set of functions of the state variables have been derived, using multiparametric programming mainly for the linear systems. Hybrid polynomial systems are considered resulting in a Mixed Integer Polynomial Programming problem. Treating the initial state of the system as a set of bounded parameters, the problem is reformulated as a multiparametric Mixed Integer Polynomial optimization (mp‐MIPOPT) problem. A novel algorithm for mp‐MIPOPT problems is proposed and the exact explicit control law for polynomial hybrid systems is computed. The key idea is the computation of the analytical solution of the optimality conditions while the binary variables are treated as relaxed parameters. Finally, using symbolic calculations exact nonconvex critical regions are computed. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3441–3460, 2016     

一种新型线性约束系统预测控制算法

通过对无约束预测控制算式的修正,应用线性规划求解技术,提出了一种基于脉冲响应模型的线性约束系统的预测控制算法。理论特性分析表明,该方法在一般情况下具有与无约束预测控制算法相同的稳定性和鲁棒性。以蒸馏塔质量控制为例进行了控制仿真,结果表明,这种新的预测控制算法不仅能满足系统存在的线性约束条件,而且有着比无约束预测控制和最优状态反馈控制更好的控制响应;与二次规划等优化算法比较,这种新的预测控制算法计算效率更高,能更好地满足生产过程实时控制需要。     

Synthesis of robust water reuse networks for single-component retrofit problems using symmetric fuzzy linear programming

Water integration techniques can be used to minimize the utility water consumption and effluent generation of process plants through the implementation of reuse or recycle networks. There are a number of graphical and mathematical programming techniques available for the synthesis of such water reuse networks. However, effective use of these methods requires the availability of reliable process data, which in reality might be difficult to acquire. This paper describes a procedure for the synthesis of robust water reuse networks from imprecise data using symmetric fuzzy linear programming (SFLP). Two model variants, one based on mass exchange units and the other on source/sink allocation, are presented. Each variant is illustrated with a numerical example.