Research Initiation: Nonlinear Predictive Process Control

化学制造工艺的经济可行性是 依赖于维持和调节系统在一些规定的 方式 保持产品规格的能力， 最小化能量或自然资源消耗是通过以下方式实现的： 使用过程控制系统。 该项目的目标是 开发使用非线性动态模型的控制系统技术 作为反馈控制的基础。 将开发一个非线性参数估计程序， 估计重要的工艺参数，如有效性因子，或 传热系数，以及过程干扰， 进料流组成。 除了参数估计， 非线性优化为基础的程序将被用来维持一个 期望的输出可变轨迹，类似于具有 在线性系统中是成功的。 过程模型， 其特征在于一组非线性微分方程，将是 转换为代数方程组使用正交配置上 有限元 序列二次规划将用于 求解代数方程下的非线性优化问题 等式约束与操纵变量和状态变量不等式 约束 这种非线性控制策略将导致更紧密的 调节重要的过程变量，从而实现更优化的 化学过程的操作。 这 研究也将导致更全面的了解， 线性预测控制技术，因为许多重要的 建议研究的影响以前没有得到解决， 线性系统案例

The economic viability of a chemical manufacturing process is dependent on maintaining and regulating the system in some prescribed manner. The ability to maintain product specifications while minimizing energy or natural resource consumption is accomplished by the use of process control systems. The goal of this project is to develop control system techniques that use a nonlinear dynamic model of the chemical process as a basis for feedback control. A nonlinear parameter estimation procedure will be developed to estimate important process parameters such as effectiveness factors or heat transfer coefficients, as well as process disturbances such as feed stream composition. In addition to parameter estimation, a nonlinear optimization-based procedure will be used to maintain a desired output variable trajectory, similar to techniques that have been proven successful for linear systems. The process model, characterized by a set of nonlinear differential equations, will be transformed to algebraic equations using orthogonal collocation on finite elements. Sequential quadratic programming will be used to solve the nonlinear optimization subject to the algebraic equation equality constraints and manipulated and state variable inequality constraints. This nonlinear control strategy will result in tighter regulation of important process variables, allowing a more optimal operation of chemical processes. This research will also result in a more complete understanding of linear predictive control techniques, since many of the important effects proposed for study have not been previously addressed for the linear system case.