Agriculture residues are a promising feedstock for value-added products from lignocellulosic waste. However, pretreatment of lignocellulosic materials is essential to facilitate enzymatic
hydrolysis and improve sugar yield. The objective of this study is to evaluate the effect of acid or alkali during microwave-assisted pretreatment of dragon fruit foliage (DFF) that
would make hydrolysis process more efficient. In the present study, distilled water and three chemicals were examined for their effects on releasing monomeric sugar during microwave
treatment. Microwave-assisted pretreatment namely microwave-distilled water (M-H2O) (control); microwave-sulfuric acid (M-H2SO4); microwave-sodium hydroxide (M-NaOH); and
microwave-sodium bicarbonate (M-NaHCO3) pretreatment were performed using 5% (w/v) of DFF as substrate at 800 watt microwave power for 5 minutes exposure time. Highest yield
of monomeric sugar was found at 15.56 mg/g using M-NaOH pretreatment at 0.1N NaOH. For M-H2SO4 pretreatment, 0.1N H2SO4 produced 8.2 mg/g of monomeric sugar. Application
of M-NaHCO3 pretreatment using 0.05N NaHCO3 solution released 6.45 mg/g of monomeric sugar. While, soaking DFF in distilled water and subjecting to microwave irradiation released
6.6 mg/g of monomeric sugar. Treatments with the lowest concentration (0.01 N) of the three chemicals released only small quantities of total monomeric sugars and less than that with distilled water. The changes in the physical structure of DFF prior to and after the microwaveassisted pretreatment are also reported.
This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min-max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC.
Inter-individual variability possesses a major challenge in the regulation of hypnosis in anesthesia. Understanding the variability towards anesthesia effect is expected to assist the design of controller for anesthesia regulation. However, such studies are still very scarce in the literature. This study aims to analyze the inter-individual variability in propofol pharmacokinetics/pharmacodynamics (PK/PD) model and proposed a suitable controller to tackle the variability. This study employed Sobol' sensitivity analysis to identify significance parameters in propofol PK/PD model that affects the model output Bispectral Index (BIS). Parameters' range is obtained from reported clinical data. Based on the finding, a multi-model generalized predictive controller was proposed to regulate propofol in tackling patient variability. [Formula: see text] (concentration that produces 50% of the maximum effect) was found to have a highly-determining role on the uncertainty of BIS. In addition, the Hill coefficient, [Formula: see text], was found to be significant when there is a drastic input, especially during the induction phase. Both of these parameters only affect the process gain upon model linearization. Therefore, a predictive controller based on switching of model with different process gain is proposed. Simulation result shows that it is able to give a satisfactory performance across a wide population. Both the parameters [Formula: see text] and [Formula: see text], which are unknown before anesthesia procedure, were found to be highly significant in contributing the uncertainty of BIS. Their range of variability must be considered during the design and evaluation of controller. A linear controller may be sufficient to tackle most of the variability since both [Formula: see text] and [Formula: see text] would be translated into process gain upon linearization.
Anaesthesia is a multivariable problem where a combination of drugs are used to induce desired hypnotic, analgesia and immobility states. The automation of anaesthesia may improve the safety and cost-effectiveness of anaesthesia. However, the realization of a safe and reliable multivariable closed-loop control of anaesthesia is yet to be achieved due to a manifold of challenges. In this paper, several significant challenges in automation of anaesthesia are discussed, namely model uncertainty, controlled variables, closed-loop application and dependability. The increasingly reliable measurement device, robust and adaptive controller, and better fault tolerance strategy are paving the way for automation of anaesthesia.