Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines

This paper deals with Wiener model based predictive control of a pH neutralization process.The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM).Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification.Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models,Laguerre-polynomial Wiener models,and linear Laguerre models.The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process.The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC,Laguerre-polynomial Wiener model based NMPC,and linear model predictive control (LMPC).Validation results show that the proposed NMPC outperforms the other three controllers.

Diversity patterns and phylogenetic structure of vascular plants along elevational gradients in a mountain ecosystem, South Korea

Recently, a phylogenetic diversity and community structure analysis as complementary to species-centric approaches in biodiversity studies provides new insights into the processes of community assembly. In this study, we analyzed species and phylogenetic diversity and community structures for woody and herbaceous plants along two elevational transects on Mt. Baekhwa, South Korea. The species richness and phylogenetic diversity of woody plants showed monotonic declining patterns with increasing elevation along all transects, whereas herbaceous plants showed different patterns, such as no relationship and a reversed unimodal pattern, between the study transects. The main drivers of these patterns were climate and habitat variables for woody and herbaceous plants, respectively. In addition, the phylogenetic community structure primarily showed phylogenetic clustering regulated by deterministic processes, especially environmental filtering, such as climate or habitat factors, along the two transects, although herbaceous plants along a transect depicted phylogenetic randomness as a result of a neutral process. Our findings suggest that deterministic and neutral processes may simultaneously control the community structures along small-scale elevational gradients such as local transects, although the deterministic process may be the predominant type.

Sparse representation based on projection method in online least squares support vector machines

A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines （LS-SVM）. The new inputs are projected into the subspace spanned by previous basis vectors （BV） and those inputs whose squared distance from the subspace is higher than a threshold are added in the BV set, while others are rejected. This consequently results in the sparse approximation. In addition, a recursive approach to deleting an exiting vector in the BV set is proposed. Then the online LS-SVM, sparse approximation and BV removal are combined to produce the sparse online LS-SVM algorithm that can control the size of memory irrespective of the processed data size. The suggested algorithm is applied in the online modeling of a pH neutralizing process and the isomerization plant of a refinery, respectively. The detailed comparison of computing time and precision is also given between the suggested algorithm and the nonsparse one. The results show that the proposed algorithm greatly improves the sparsity just with little cost of precision.