Department of Chemical Engineering

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Lecture Notes in Mechanical Engineering

Preface

Causal analysis plays a vital role in determining the underlying relationship among the variables in a system from the data. In this article, the sparse inverse covariance (SIC) estimation is coupled with likelihood score, and a two-step approach is proposed to address the problem of causal analysis. The estimation of SIC matrix for undirected sparse network reconstruction is performed with the L0 -norm constraint in the framework of greedy sparse simplex (GSS) algorithm. Furthermore, the GSS algorithm is suitably modified to incorporate the additional constraint of positive semidefiniteness of the inverse covariance matrix. To determine the causal direction among the variables, the likelihood score is computed for the associated variables in the reconstructed network in the second step. The efficacy of the proposed approach for causal analysis is illustrated using numerical examples and an industrial application on prediction of flooding and weeping in a deethanizer column associated with a fluid catalytic cracking unit. From these studies, it is observed that the proposed approach is able to recover causal connections accurately in both cases. Furthermore, the probable reasons for the occurrence of flooding and weeping phenomena in an industrial deethanizer unit are also inferred from the identified causal network. © 2022 IEEE.

IEEE Transactions on Control Systems Technology

Sparse Inverse Covariance Estimation for Causal Inference in Process Data Analytics

Multi-agent systems are usually large-scaled with a growing degree of intelligence and integration. Direct applications of traditional (centralized) methods will become incompetent for effective process monitoring of multi-agent systems. It necessitates the cognitive learning strategies that determine the effective interactions among subsystems or individuals. Therefore, in order to improve monitoring performance, this paper targets the development of a new distributed process monitoring method that has the cognitive learning ability by embedding an adaptive pickup rule. The proposed cognitive learning-based method can reduce the computation loads in both off-line and online phases because only necessary information exchange (or communication topology) is involved. Furthermore, the threshold used for system monitoring is obtained by developing a fast search algorithm based on statistical learning theory. Case studies on the wastewater treatment system, which can be regarded as a typical multi-agent system, demonstrate the superiority of the proposed distributed process-monitoring method. IEEE

IEEE Transactions on Cognitive and Developmental Systems

Distributed Process Monitoring for Multi-Agent Systems Through Cognitive Learning

In this paper, a self-optimization algorithm is developed to find both the optimal operating point and the path from the current condition to the optimal point. Being a model-based strategy, a generalized locally weighted probabilistic principal component regression (PPCR) model that is robust to outliers and can handle missing data, is developed to model the plant. To account for the model-plant mismatch, a penalty term in the form of a robust Gaussian process regression is incorporated into the optimization process. A non-linearity index is utilized to control the accuracy of the local model. Finally, the exploration in optimization for diversity through the acquisition functions is studied. The performance of the proposed algorithm is demonstrated on a simulation case study of a deethanizer column. © 2022 Elsevier B.V.. All rights reserved.

IFAC-PapersOnLine

Data-driven self-optimization of processes in the presence of the model-plant mismatch

A facile and cost-effective modified sol–gel synthesis is employed to synthesize flexible silica-cellulose hybrid aerogels (SCHA) using recycled cellulose fibers (RCF). The effect of cellulose fiber concentrations and ambient temperature conditions on the thermal, acoustic, and oil absorption characteristics was quantified comprehensively. The experiments were conducted by considering the range of weight fractions from 1–4 wt.% from waste tissue paper with a cross-linker and tetraethylorthosilicate (TEOS) as silica precursors. The SCHA aerogels were modified by a silylating agent with surface hydroxyl groups to achieve superhydrophobic behavior with a water contact angle (WCA) as high as 168°. The synthesized aerogels were characterized by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), scanning electron microscope (SEM), and Brunauer-Emmett-Test (BET) to identify the physical morphology. An excellent oil absorption capacity of 24.8 g.g−1 was also noticed with 4 wt.% of cellulose concentration in hybrid aerogel retained up to 5 cycles with an absorption capacity of 6.4 g.g−1. The average thermal conductivity of hybrid aerogels was estimated in the range of 0.038–0.032 W/m K. Slight enhancement in the thermal conductivity is noted with the increase in wt.% of cellulose to the silica aerogel. However, about 40–50 °C improvement in thermal degradability (as concluded from Thermogravimetric Analysis) with a minimum weight loss was observed in hybrid aerogel over cellulose aerogel. A comparatively high sound absorption coefficient of 0.453–0.628 at low frequency (1500 Hz) and 0.86–0.94 at high frequency (3600 Hz) was achieved with an average thickness of 8 mm compared to cellulose aerogel. The compressive Young’s modulus of hybrid aerogels was also enhanced by 94.12%. Graphical abstract: [Figure not available: see fulltext.] © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Journal of Materials Science

Superhydrophobic hybrid silica-cellulose aerogel for enhanced thermal, acoustic, and oil absorption characteristics

In this study, the lattice Boltzmann method is used to explore the double-diffusive natural convection within a shallow enclosure with a built-in rectangular blockage at its geometric center. The enclosure is exposed to an external magnetic field. One of the enclosure walls is subjected to linearly/sinusoidal heating and concentration conditions. The rectangular blockage is at higher temperature and concentration conditions. Numerical experimentation is executed for a range of parameters such as Rayleigh number (Formula presented.), Lewis number (Formula presented.), buoyancy ratio (Formula presented.), aspect ratio of the enclosure (Formula presented.), aspect ratio of the built-in blockage (Formula presented.), and Hartmann number (Formula presented.). The working fluid is assumed to be liquid metal (sodium–potassium alloy, (Formula presented.)). The complex interaction of flow parameters with the nonuniform heating and concentration conditions has been explored. Higher heat and mass transfer (HMT) rates are noticed for the sinusoidal temperature and concentration profile compared to linear for a given (Formula presented.). (Formula presented.) and (Formula presented.) showed enhancement by (Formula presented.), and (Formula presented.) for sinusoidal temperature and concentration profile of the enclosure as compared to linear for (Formula presented.). The increase in the blockage and the enclosure aspect ratio shows augmentation in the HMT rate. The consequences of the study are also summarized in terms of empirical correlations of the (Formula presented.) and (Formula presented.) numbers showing functional dependence on the considered range of parameters. © 2022 Wiley Periodicals LLC.

Heat Transfer

Effect of the aspect ratio of the shallow enclosure and built-in rectangular blockage on MHD double-diffusive free convection subjugated to non-uniform boundary conditions

Model predictive control is a popular advanced control method. The detection and diagnosis of model-plant-mismatches is an important task to ensure that an MPC is operating optimally and any potential model re-identification is targeted to only the sub-models that need it. Conventional detection methods directly use plant operating data for such purposes. Such methods fail in the presence of significant disturbances. A slow feature analysis data reconstruction is proposed to remove fast and typically irrelevant variations, extracting only those slow-varying and important components of the data to detect model-plant-mismatches. It is shown to have improved performance over a conventional method through both simulated and industrial case studies, and thus provide a more targeted selection of sub-models that need re-identification. © 2022 IEEE.

2022 IEEE International Symposium on Advanced Control of Industrial Processes, AdCONIP 2022

MPC Model-Plant-Mismatch Detection Through Slow Feature Analysis Preprocessing with Industrial Application

In the present work, the influence of the aspect ratio of the rectangular cavity (length &gt; width) and the built-in rectangular blockage (length &gt; width) on the local and global entropy generation (EG) characteristics due to double-diffusive convection and external magnetic field have been explored. Particularly, the effect of the pertinent parameters, such as the aspect ratio of the cavity (Formula presented.) and blockage (Formula presented.) Lewis number (Formula presented.) Rayleigh number (Formula presented.) Hartmann number (Formula presented.) and buoyancy ratios (Formula presented.) on the EG characteristics have been explored. The working fluid is considered to be a liquid metal-sodium–potassium alloy (Pr = 0.054). Lattice Boltzmann method numerical scheme is employed to solve the double-diffusive natural convection phenomenon. The increase in Ra induces an enhancement in the localized EG terms, whereas the augmentation in Ha diminishes the local total EG (S Total) for a given Ra. The dominance of thermal and species transport irreversibility (Formula presented.) in the cavity was noticed for (Formula presented.) Conversely, for AR = 4, the significance of flow friction irreversibility within the cavity was observed ((Formula presented.)). The results of the work are also summarized in terms of simpler empirical correlations of (Formula presented.) and ST, which might be useful for possible engineering design purposes. © 2022 Taylor & Francis Group, LLC.

Numerical Heat Transfer; Part A: Applications

Entropy generation study due to MHD double-diffusive convection in the rectangular cavity with built-in rectangular blockage

The availability of high-quality, low-cost water to meet requirements of the human has become a major issue in the twenty-first century. Supply of fresh water systems throughout the globe are failing to keep up with increasing demand, which has also been made worse by human population growth, global warming, and other factors and decreasing water quality in recent years. No amount of emphasis can be placed on the significance of technological innovation in allowing integrated water resource management. Nanotechnology promises to improve water and waste-water treatment efficiency. Due to its unique features, including as very tiny size, high surface-area-to-volume ratio, the surface’s potential to be modified, good magnetic characteristics, as well as a high level of biocompatibility, the use of iron oxide nanoparticles has gotten a lot of interest. Iron oxide nanoparticles have been propose as nano absorbents and photo catalysts in various environmental cleanup methods for waste-water treatment. This study described the most recent iron oxide nanoparticles uses in waste-water treatment, as well as the gaps that have hampered their large-scale field usage. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Environmental Science and Engineering

Ferrofluids for Waste-Water Treatment

Over the years, the lattice Boltzmann method (LBM) has been evolved as a substitute and efficient numerical tool to mimic the single/multiphase fluid flow and transport problems. LBM has been mainly advantageous in multi-physics and multiphase flow applications. On the other hand, double-diffusive convection has extensive occurrence in domestic and industrial activities. The type of convection in which the combined effect of temperature and concentration gradients (resulting in the density variation) on fluid's hydrodynamic and thermal characteristics is called double-diffusive convection (DDC). The importance of DDC has been recognized in various engineering applications, and it has thoroughly been investigated experimentally, theoretically, and numerically. This paper is proposed to deliver a brief review of double-diffusive convection by computational approach (mainly lattice Boltzmann method and Navier–Stokes equation-based solvers). This review explores the illustration of some of the practical applications of DDC, studies of DDC in various heated cavities. The paper also gives insights into LBM formulation of DDC under various external force conditions. A table compromising various empirical correlations of the average Nusselt numbers and average Sherwood numbers as a function of different governing parameters has been discussed. © 2022, Akadémiai Kiadó, Budapest, Hungary.