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This article concerns the effectiveness of various types and degrees of surface modification of sisal fibers involving dewaxing, alkali treatment, bleaching cyanoethylation and viny1 grafting in enhancing the mechanical properties, such as tensile, flexural and impact strength, of sisal‐polyester biocomposites. The mechanical properties are optimum at a fiber loading of 30 wt%. Among all modifications, cyanoethylation and alkali treatment result in improved properties of the biocomposites. Cyanoethylated sisal‐polyester composite exhibited maximum tensile strength (84.29 MPa). The alkali treated sisal‐polyester composite exhibited best flexural (153.94 MPa) and impac strength (197.88 J/m), which are, respectively, 21.8% and 20.9% higher than the corresponding mechanical properties of the untreated sisal‐polyester composites. In the case of vinyl grafting, acrylonitrile (AN)‐grafted sisal‐polyester composites show better mechanical properties than methyl‐methacrylate (MMA)‐grafted sisal composites. Scanning electron microscopic studies were carried out to analyze the fiber‐matrix interaction in various surface‐modified sisal‐polyester composites.

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This work aims at the identification of a special class nonlinear state space observers for nonlinear multivariable systems directly from input-output data when the data is corrupted with unmeasured disturbances. At the identification stage, the one step ahead predictor form of the model is arranged to have a Weiner-like structure. The linear dynamic component of the predictor is parametrized using generalized orthonormal basis functions. The resulting observer is shown to be a nonlinear ARX (NARX) type model with an infinite but fading memory property. It is also shown that the proposed model structure is capable of capturing input as well as output multiplicity (multiple steady states) behavior. The efficacy of the proposed modeling scheme is demonstrated using simulation studies on a continuously stirred tank reactor (CSTR) process model, which exhibits input multiplicity, and another CSTR process model that exhibits output multiplicities. The types of unmeasured disturbances investigated are (a) unknown input disturbances (such as feed concentration fluctuations), (b) uncertainties in manipulated inputs, and (c) fluctuation in process parameters. The proposed modeling scheme is also validated in real time using a laboratory scale, multivariable experimental system. The analysis of the simulation and experimental studies reveals that the identified models have excellent disturbance modeling and long range prediction abilities. The identified models are also able to capture the steady-state behavior of the systems under consideration reasonably accurately over a wide operating range. The resulting stochastic model can be directly used for the development of an extended Kalman filter and to formulate a nonlinear model predictive control (NMPC) scheme.