Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

Distributed control of chemical process networks

In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area

Magnesium methanesulfonate salt found in the Dome Fuji (Antarctica) ice core

Using micro-Raman spectroscopy, we identified the chemical forms of methanesulfonate salt particles in reference samples of the Dome Fuji (Antarctica) ice core. We found only (CH3SO3)2Mg・nH2O among methanesulfonate salts, and this salt particle is most prevalent in the Last Glacial Maximum (LGM) ice. We suggest that during the LGM, (CH3SO3)2Mg・nH2O may have formed in the atmosphere through the chemical reaction of CH3SO3H with sea salts, but probably not in the firn and ice due to the neutralization of acid in LGM ice of inland Antarctica