Pressure swing adsorption (PSA) technology is used in various applications. PSA is a cost-effective process with the ability to produce high-purity bio-hydrogen (99.99%) with high recovery rates. In this article, a PSA process for the production of bio-hydrogen is proposed; it uses two columns packed with type 5A zeolite, and it has a four-step configuration (adsorption, depressurization, purge, and repressurization) for bio-hydrogen production and regeneration of the beds. The aim of this work is to design and use an active fault-tolerant control (FTC) controller to raise and maintain a stable purity of 0.9999 in molar fraction (99.99%), even with the occurrence of actuator faults. To validate the robustness and performance of the proposed discrete FTC, it has been compared with a discrete PID (proportional–integral–derivative) controller in the presence of actuator faults and trajectory changes. Both controllers achieve to maintain stable purity by reducing the effect of faults; however, the discrete PID controller is not robust to multiple faults since the desired purity is lost and fails to meet international standards to be used as bio-fuel. On the other hand, the FTC scheme reduces the effects of individual and multiple faults by striving to maintain a purity of 0.9999 in molar fraction and complying with international standards to be used as bio-fuel.

This paper introduces a new approach of the mean Euler-Poincaré characteristic for non-Gaussian random fields (NGRF), which is based on the decomposition by a basic function named motherwave. The method is proved for long-term recorded, noisy physiological signals. A pretreatment allows the signal to become smooth as the original one is fitted through a Random Algebraic Polynomials (RAP)-based scheme. After that, the polynomized signals are merged by thresholding the RAP function at different levels u. In this way, it is formed a real-valued non-Gaussian physiological random field (NGPRF). Thereby, we deal with their geometric properties centered on their excursion sets A u (Φ, T ) and a topological invariant, such as the Euler Poincaré Characteristic (EPC) ϕ(A u (Φ, T )). The highlight of this work is an explicit model, referred to as the decomposed mean Euler-Poincaré characteristic (DMEPC). The proposed method produces a reduced model with a viable interpretation for different heart conditions investigated for data issued from Holter EKG recordings.