Electrical Resistance Tomography (ERT) is a technique employed in the industrial environment that reconstructs images formed by the impedance distributions inside a vessel. The method relies on measures from a data acquisition system, responsible to inject a current inside the vessel and measures the voltage on the boundaries, and sequentially an inverse problem solution. This paper introduces a low-cost design of an acquisition system that can obtain 30 images/s with a signal-to-noise ratio of 57.47 dB and a maximum error of 3.79%. It utilizes a simple RMS-to-DC converter demodulation scheme to provide good accessibility to researchers aimed at investigating flow patterns of multiphase flow applications.

The increase in emissions of toxic gasses such as hydrogen sulfide (H₂S) and carbon dioxide (CO₂), resulting from growing urbanization and industrialization, has caused environmental and public health problems, making the implementation of air purification techniques through adsorption important. Thus, modeling the gas adsorption process is fundamental for good agreement with experimental data, employing mathematical models that enable the prediction of adsorption capacity. In this way, the present work aimed to compare different analytical breakthrough curve models (Thomas, Yoon–Nelson, Adams–Bohart, and Yan) for the adsorption of H₂S and CO₂ in fixed-bed columns, using experimental data from the literature, estimating the curve parameters through the Markov Chain Monte Carlo (MCMC) method with the Metropolis–Hastings algorithm, and ranking using the determination coefficients (R² and R²_Adjusted) and the Bayesian Information Criterion (BIC). The models showed better agreement using the estimation of maximum adsorption capacity (q_s, N₀) and the constants of each model (k_th, k_yn, and k_ba). In the adsorption of H₂S, the Yan model stood out for its precision in estimating q_s. For the adsorption of CO₂, the Adams–Bohart model achieved better results with the estimation of N₀, along with the Yoon–Nelson model. Furthermore, the use of this method allows for a reduction in computational effort compared to models based on complex differential equations.

In many thermal engineering problems involving high temperatures/high pressures, the boundary conditions are not fully known since there are technical difficulties in obtaining such data in hostile conditions. To perform the task of estimating the desired parameters, inverse problem formulations are required, which entail to performing some extra measurements of certain accessible and relevant quantities. In this paper, justified also by uniqueness of solution conditions, this extra information is represented by either local or non-local boundary temperature measurements. Also, the development of numerical methods for the study of coefficient identification thermal problems is an important topic of research. In addition, in order to decrease the computational burden, meshless methods are becoming popular. In this article, we combine, for the first time, the method of fundamental solutions (MFS) with a particle filter sequential importance resampling (SIR) algorithm for estimating the time-dependent heat transfer coefficient in inverse heat conduction problems. Two different types of measurements are used. Numerical results indicate that the combination of MFS and SIR shows high performance on several test cases, which include both linear and nonlinear Robin boundary conditions, in comparison with other available methods.

The use of biomass has been promoted to meet the need for sustainable production of ethylene, which is the most used petroleum-derived in the polymer industry. Ethanol is an alternative feedstock to yield the so-called bio-ethylene through catalytic dehydration in fixed-bed reactors. As the reaction system is strongly endothermic, it is very important to know accurately the reactor temperature to assure the process performance. However, in the industrial context, the process measurements are often uncertain and not all variables can be directly measured online. In this regard, this paper analyses the mathematical modelling and numerical simulation of the ethanol catalytic dehydration and contributes with a monitoring scheme using the Bayesian method known as particle filter. Numerical simulations helped understanding the process behaviour and locating the best position for the temperature sensor in the reactor. From temperature measurements, the proposed inferential tool estimates hidden state variables and unmeasured disturbances, using Sequential Importance Resampling algorithm for the particle filter. The proposal is investigated according to the number of particles and the criterion total error reduction. The results show that the monitoring scheme is able to estimate satisfactorily the process variable profiles, as the temperature and chemical conversion, along the reactor length.