Improvement of De-NOx Device Control Performance Using Software Sensor

Matsumura, Shiro; Iwahara, T.; Suzuki, M.; Shioya, H.; Koide, T.

doi:10.1016/s1474-6670(17)43044-8

Cited by 9 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Software sensors have been described for diverse applications, such as chemical reactors (Régnier et al, 1996), analytical devices (Matsumura et al, 1998), environmental processes (Carstensen et al, 1996), and bioprocesses (Kiviharju et al, 2008). In addition, software sensors support the ambition of process analytical technology (PAT) to establish on-line monitoring methods for high quality manufacture of pharmaceutical products (FDA, 2004;Gnoth et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein

Warth

Rajkai²,

Mandenius

2010

Journal of Biotechnology

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein

Warth

Rajkai²,

Mandenius

2010

Journal of Biotechnology

View full text Add to dashboard Cite

“…From (7), the output prediction may be also described as the following form: (14) where (15) Define the control move sequence and the desired output sequence as follows:…”

Section: B Nonlinear Mpc Strategymentioning

confidence: 99%

“…Now let us consider the following optimization problem, shown in equation (18) at the bottom of the page, where , represents the identity matrix, and are the positive definite diagonal weighting matrices, and both control levels and control moves are penalized. Introducing (15) into (18), after removing the constant terms, the quadratic form of the optimization problem (18) could be obtained as shown in (19)-(23) at the bottom of the page. The online optimization problem (19) or (20) may be solved by the quadratic programming (QP) routines.…”

Section: B Nonlinear Mpc Strategymentioning

confidence: 99%

Nonlinear Predictive Control Using Neural Nets-Based Local Linearization ARX Model—Stability and Industrial Application

Peng

Nakano

Shioya³

2007

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

A Gaussian radial basis function (RBF) neural networks-based local linearization autoregressive with exogenous (ARX) model is utilized for describing the dynamics of a class of smooth nonlinear and nonstationary industrial processes. The dynamics of the underlying processes may be treated as the system operating-point-dependent time-varying locally-linear behavior. The RBF-ARX model is a pseudo-linear ARX model identified offline, and its functional coefficients are composed of the operating-point-dependent RBF neural networks. The RBF-ARX model-based predictive control (MPC) design to the nonlinear process is presented, and stability analysis of the nonlinear MPC under some conditions is discussed. Especially, the feasibility and effectiveness as well as the significant performance improvements of the nonlinear MPC design proposed is demonstrated with a real industrial application to the nitrogen oxide (NO x ) decomposition (de-NO x ) process in thermal power plants.

show abstract

“…1 shows the structure diagram of a de-NO x process in a thermal power plant. The dynamic behavior of this process is nonlinear and non-stationary, and depends on the timevarying load demand w(t) of the power plant (Matsumura et al, 1997). At different load levels, the process dynamics (between the de-NO x device outlet NO x y(t) and the total NH 3 flow-set u(t)+v 2 (t) in Fig.…”

Section: Simulation Studymentioning

confidence: 99%