The principal component regression
(PCR) based soft sensor modeling technique has been widely used for
process quality prediction in the last decades. While most industrial
processes are characterized with nonlinearity and time variance, the
global linear PCR model is no longer applicable. Thus, its nonlinear
and adaptive forms should be adopted. In this paper, a just-in-time
learning (JITL) based locally weighted kernel principal component
regression (LWKPCR) is proposed to solve the nonlinear and time-variant
problems of the process. Soft sensing performance of the proposed
method is validated on an industrial debutanizer column and a simulated
fermentation process. Compared to the JITL-based PCR, KPCR, and LWPCR
soft sensing approaches, the root-mean-square errors (RMSE) of JITL-based
LWKPCR are the smallest and the prediction results match the best
with the actual outputs, which indicates that the proposed method
is more effective for quality prediction in nonlinear time-variant
processes.
Self-optimizing control (SOC) constitutes
an important class of
control strategies for real-time optimization (RTO) of chemical plants,
by means of selecting appropriate controlled variables (CVs). Within
the scope of SOC, this paper develops a CV selection methodology for
a global solution which aims to minimize the average economic loss
across the entire operation space. A major characteristic making the
new scheme different from existing ones is that each uncertain scenario
is independently considered in the new solution without relying on
a linearized model, which was necessary in existing local SOC methods.
Although global CV selection has been formulated as a nonlinear programming
(NLP) problem, a tractable numerical algorithm for a rigorous solution
is not available. In this work, a number of measures are introduced
to ease the challenge. First, we suggest representing the economic
loss as a quadratic function against the controlled variables through
Taylor expansion, such that the average loss becomes an explicit function
of the CV combination matrix, and a direct optimizing algorithm is
proposed to approximately minimize the global average loss. Furthermore,
an analytic solution is derived for a suboptimal but much more simplified
problem by treating the Hessian of the cost function over the entire
operating space as a constant. This approach is found to be very similar
to one of the existing local methods, except that a matrix involved
in the new solution is constructed from global operating data instead
of using a local linear model. The proposed methodologies are applied
to two simulated examples, where the effectiveness of the proposed
algorithms is demonstrated.
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