A Soft Sensor for Biomass in a Batch Process with Delayed Measurements

This paper presents a soft sensor to estimate the biomass concentration in a batch bioprocess used in production of δ-endotoxins of Bacillus thuringiensis, subject to delayed measurements. The soft sensor proposed is based on a cascade observer-predictor algorithm. The observer stage is based on a class of second order sliding mode algorithms, allowing a fixed time estimation of the biomass. Additionally, the prediction stage offsets the effect of the delay in measurements. Simulations show the feasibility of the proposed observer.Universidad Nacional de ColombiaCINVESTAV-IPNITESO, A.C

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA

Feedback Linearizing Control Strategies for Chemical Engineering Applications.

Two widely studied control techniques which compensate for process nonlinearities are feedback linearization (FBL) and nonlinear model predictive control (NMPC). Feedback linearization has a low computational requirement but provides no means to explicitly handle constraints which are important in the chemical process industry. Nonlinear model predictive control provides explicit constraint compensation but only at the expense of high computational requirements. Both techniques suffer from the need for full-state feedback and may have high sensitivities to disturbances. The main work of this dissertation is to eliminate some of the disadvantages associated with FBL techniques. The computation time associated with solving a nonlinear programming problem at each time step restricts the use of NMPC to low-dimensional systems. By using linear model predictive control on top of a FBL controller, it is found that explicit constraint compensation can be provided without large computational requirements. The main difficulty is the required constraint mapping. This strategy is applied to a polymerization reactor, and stability results for discrete-time nonlinear systems are established. To alleviate the need for full-state feedback in FBL techniques it is necessary to construct an observer, which is very difficult for general nonlinear systems. A class of nonlinear systems is studied for which the observer construction is quite easy in that the design mimics the linear case. The class of systems referred to are those in which the unmeasured variables appear in an affine manner. The same observer construction can be used to estimate unmeasured disturbances, thereby providing a reduction in the controller sensitivity to those disturbances. Another contribution of this work is the application of feedback linearization techniques to two novel biotechnological processes. The first is a mixed-culture bioreactor in which coexistence steady states of the two cell populations must be stabilized. These steady states are unstable in the open-loop system since each population competes for the same substrate, and each has a different growth rate. The requirement of a pulsatile manipulated input complicates the controller design. The second process is a bioreactor described by a distributed parameter model in which undesired oscillations must be damped without the use of distributed control