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In this paper, using a suitable model for propane thermal cracking and based on a cost function, the optimal temperature profile is obtained. To achieve the optimal temperature trajectory, the furnace is divided into several zones and proportional-integral-derivative controllers are used to control the temperature of each zone. According to temperature measurements available, two cases are considered. In the first case, it is assumed that the furnace wall temperatures at different points are measured along with the reactor outlet gas temperature. In the second case, we assume that the reacting gas temperatures inside the reactor at different points are available.Simulation results indicate that for the first case the optimal temperature profile can be achieved if an accurate model is available. In the case of model mismatch, a method has been proposed which results in a near optimal profile. For the second case, two different strategies for set point tracking are proposed and their performances are compared through simulation. The controller performance for load rejection is also evaluated.

Nanotechnology is gaining more attention in biotechnological applications as a research area with a huge potential. Nanoparticles (NPs) can influence the rate of anaerobic digestion (AD) as the nano-sized structures, with specific physicochemical properties, interact with substrate and microorganisms. The present work has classified the various types of additives used to improve the AD processes. Nanomaterials as new additives in AD process are classified into four categories: Zero-valent metallic NPs, Metal oxide NPs, Carbon based nanomaterials, and Multi-compound NPs. In the following, application of nanomaterials in AD process is reviewed and negative and positive effects of these materials on the AD process and subsequently biogas production rate are discussed. This study confirms that design and development of new nano-sized compounds can improve the performances of the AD processes.

Summary In this paper, the design of an adaptive tracking control for a class of switched uncertain multiple‐input–multiple‐output nonlinear systems in the strict‐feedback form with unmodeled dynamics in the presence of three types of input nonlinearity under arbitrary switching has been addressed. By means of an intelligent approximator like a fuzzy logic system or a neural network, the unknown dynamics are estimated. The unmodeled dynamics have been tackled with a dynamic signal. A universal framework for describing different types of input nonlinearity including saturation, backlash, and dead zone has been utilized. By applying the backstepping approach and the common Lyapunov function method, virtual and actual controllers and the adaptive law for each subsystem have been constructed. Finally, it has been shown that the closed‐loop system is semiglobally uniformly ultimately bounded and the tracking errors converge to their predefined bounds. The effectiveness of the proposed control scheme has been shown through simulation study.

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