The non-linear process control is the most important problem of statement in chemical industries. Stirred tanks are frequently used as industrial reactors, where a chemical component of a flow stream resides in the tank for a period of time before proceeding to other steps in a chemical process. In this research article, a computational intelligence-based controller is introduced for CSTR concentration control model. The proposed RBFNN model is optimally tuned by two-hybrid optimization strategies based on combining the best features of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and their variants such as Deterministic Particle Swarm Optimization Algorithm, and Differential Gravitational Search algorithm (DPSO-DGSA). An experiment is conducted to examine the effectiveness of the proposed controller methodology to compare it to other state-of-the-art approaches.

A model describing the main processes occurring in the UASB reactor was developed; in order to maintain simplicity and applicability of the model, only the fundamental aspects were considered. In the model, the reactor is divided in several well-stirred reactors coupled in series and it comprises substrate degradation, biomass growth and the reactions that take place within the granules. The important contribution of the paper is the development of a model taking into account the mass transfer through the film around the granules, the intra-particle diffusion, and the degradation reaction. The model enables the determination of the removal efficiency of the substrate and the increase of both the height of the sludge bed and the granule size with time. The simulated results of an experimental UASB reactor treating sugar-cane mill wastewater were found to be in good agreement with the performance of the reactor. The sensitivity analysis shows that the performance of the reactor is determined by several parameters. The most important parameters are: the bioconversion rate, the mass transfer coefficient in the film, the intra-particle diffusivity, the volumetric fraction of biomass in the reactor, and the number of CSTR considered. These parameters should therefore be carefully determined. The model could be a useful tool in the optimization and development of UASB reactors.

The aim of present study was to investigate the removal of lead and copper from textile wastewater using waste egg shells in a continuous stirred tank reactor. The effect of initial pH, metal concentration, adsorbent dosage and retention time were investigated. Optimum adsorption of lead (80%) was obtained at pH 6, initial metal concentration 20 mg/L, adsorbent dosage 12. 5 g/L and retention time 90 minute. Also, optimum adsorption of copper (71%) was obtained at pH 6, initial metal concentration 15 mg/L, adsorbent dosage 15 g/L and retention time 75 minute. Langmuirs, Freundlich and Temkin isotherms were used for the mathematical description of adsorption equilibrium. Langmuir isotherm showed the best fitting to the isotherm equilibrium data, with a maximum adsorption capacity (qm) of 4. 33 mg/g and 3. 54 mg/g for lead and copper, respectively. Results revealed that pseudo-second order adsorption kinetic equation fit the data with a high correlation coefficient (R2 more than 0. 97). Based on the results of the present study, egg shells is suitable to be used for the removal of lead and copper from textile wastewater.

This paper aims to investigate the robust control problem of Continuously Stirred Tank Reactors (CSTR). A CSTR is one of the most essential pieces of equipment in chemical processes, whose effects of highly nonlinear dynamic and external disturbances make it very difficult to be controlled. Firstly, a novel finite-time sliding mode control is introduced that eliminates disturbance effects and ensures finite-time tracking. Secondly, to better compensate for disturbances and to improve controller performance, a finite-time disturbance observer is developed. Finally, an adaptive robust control method is introduced based on the proposed sliding mode control and the disturbance observer. Stability analysis is performed to investigate the finite-time tracking of the closed-loop system under the proposed controllers. Besides, to enhance the performance of the proposed controllers, the design parameters are tuned by the genetic optimization algorithm. Simulation results are produced to confirm the efficiency of the proposed methods in terms of tracking errors and convergence rates. The proposed finite-time sliding mode control and the adaptive finite-time sliding mode control with settling times of 1. 73s and 1. 71s as well as IAE of 0. 509 and 0. 4843, respectively, showed more desirable performance than other controllers.

A Fault-Tolerant Control (FTC) methodology has been presented for nonlinear processes being imposed by control input constraints. The proposed methodology uses a combination of Feedback Linearization and Model Predictive Control (FLMPC) schemes. The resulting constraints in the transformed process will be dependent on the actual evolving states, making their incorporation in the design context a non-trivial task. A feasible direction method has been integrated in the design procedure based on active set technique to resolve the challenging constraint–based FLMPC problem. The formulated FLMPC design method is utilized to develop a FTC scheme by providing a set of backup control configurations for a CSTR benchmark process. The successful performance of the proposed FTC methodology has been demonstrated via a category of common fault scenarios by exercising an arbitrary replacement of control configurations through a supervisor to maintain the CSTR operation at an unstable desired steady-state point.