By Doris Sáez MSc, PhD, Aldo Cipriano PhD, Andrzej W. Ordys PhD (auth.)
In the more and more aggressive smooth global, the commercial quarter faces new demanding situations equivalent to bettering productiveness and decreasing expenses whereas making an allowance for the method operational constraints.
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Extra info for Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants
In the general case, the system may be multi variable, therefore r, w, y, u and e are vectors. 1. A two-level control diagram Next, the main components of the supervisory control design procedure are described. , 1987). +anaq-na, B(q-I)=b1q-I+"'+bnbq-nb and A=I-q-I. Also, e(t) is a zero mean white noise and q-I is the backward shift operator (q -I x(t) = x(t -1)). 2 Modelling of the Regulatory Level As mentioned before, optimisation of the plant operation is solved by adding a supervisory optimal level without modifying the regulatory level.
Otherwise, numerical optimisation is needed. , 1987) and dynamic matrix control (DMC) (Cutler and Ramaker, 1980), will be described. , 1987) that is appropriate for many industrial processes in which disturbances are non-stationary. A CARlMA SISO model is given by: Non-linear Predictive Control with A(q-l) = 1+ alq-l + ... + anaq-na , B(q-l) = b1q-l + ... + bnbq-nb 39 and il=l_q-l. Also, e(t) is a zero mean white noise and q-l is the backward shift operator (q-1x(t) = x(t-l)). 10) where Ej and Fj are uniquely defined polynomials, of degree j-I and na, respectively, given A(q-l) and the prediction horizonj.
However, this method implies more computing effort. Van der Veen et al. (1999), Basbuska et al. (1999) and others describe the Branch and Bound optimisation algorithm in order to solve the global optimisation problem of the predictive control based on fuzzy models. Nounou and Passino (1999) formulate a new fuzzy predictive algorithm by proposing a control action that is calculated for the lineal rule with the highest satisfaction degree at the present sampling time. This algorithm is favourably compared to the Cipriano and Ramos (1995) and Roubos et al.
Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants by Doris Sáez MSc, PhD, Aldo Cipriano PhD, Andrzej W. Ordys PhD (auth.)