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Soda lignin from oil palm empty fruit bunch was directly isolated by various mineral acids i.e. sulfuric acid, hydrochloric acid, phosphoric acid and nitric acid at three levels of concentration (20% v/v, 60%v/v and concentrated). A comparison study was performed through physicochemical properties and structural features using FT-IR, UV, 13C-NMR and nitrobenzene oxidation. The FT-IR results showed that there is no significant difference between the main structures of the lignin isolated by various acids. However, low concentration of phosphoric acid is preferable because of its highest yield. The S: V: H ratio of 7-15:6-11:1 as evaluated by the nitrobenzene oxidation procedure suggests that soda lignin can be classified as belonging to either the cereal straw on grass type. The UV results indicate that phosphoric acid consistently gave the highest absorbance value among the four acids tested in this study regardless of its concentration level. The C13-FTNMR spectra, suggest that the lignin structure is independent of the type of acid used for precipitation.

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.