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Neural network modeling of the kinetics of SO2 removal by fly ash-based sorbent

Raymond-Ooi EH, Lee KT, Mohamed AR, Chu KH

J Environ Sci Health A Tox Hazard Subst Environ Eng, 2006;41(2):195-210.
PMID: 16423725

The mechanistic modeling of the sulfation reaction between fly ash-based sorbent and SO2 is a challenging task due to a variety reasons including the complexity of the reaction itself and the inability to measure some of the key parameters of the reaction. In this work, the possibility of modeling the sulfation reaction kinetics using a purely data-driven neural network was investigated. Experiments on SO2 removal by a sorbent prepared from coal fly ash/CaO/CaSO4 were conducted using a fixed bed reactor to generate a database to train and validate the neural network model. Extensive SO2 removal data points were obtained by varying three process variables, namely, SO2 inlet concentration (500-2000 mg/L), reaction temperature (60-80 degreesC), and relative humidity (50-70%), as a function of reaction time (0-60 min). Modeling results show that the neural network can provide excellent fits to the SO2 removal data after considerable training and can be successfully used to predict the extent of SO2 removal as a function of time even when the process variables are outside the training domain. From a modeling standpoint, the suitably trained and validated neural network with excellent interpolation and extrapolation properties could have immediate practical benefits in the absence of a theoretical model.
Optimizing the specific surface area of fly ash-based sorbents for flue gas desulfurization

Lee KT, Bhatia S, Mohamed AR, Chu KH

Chemosphere, 2006 Jan;62(1):89-96.
PMID: 15996711

High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
Development of the clinical assessment scale in autoimmune encephalitis

Lim JA, Lee ST, Moon J, Jun JS, Kim TJ, Shin YW, et al.

Ann Neurol, 2019 03;85(3):352-358.
PMID: 30675918 DOI: 10.1002/ana.25421

OBJECTIVE: There is no scale for rating the severity of autoimmune encephalitis (AE). In this study, we aimed to develop a novel scale for rating severity in patients with diverse AE syndromes and to verify the reliability and validity of the developed scale.
METHODS: The key items were generated by a panel of experts and selected according to content validity ratios. The developed scale was initially applied to 50 patients with AE (development cohort) to evaluate its acceptability, reproducibility, internal consistency, and construct validity. Then, the scale was applied to another independent cohort (validation cohort, n = 38).
RESULTS: A new scale consisting of 9 items (seizure, memory dysfunction, psychiatric symptoms, consciousness, language problems, dyskinesia/dystonia, gait instability and ataxia, brainstem dysfunction, and weakness) was developed. Each item was assigned a value of up to 3 points. The total score could therefore range from 0 to 27. We named the scale the Clinical Assessment Scale in Autoimmune Encephalitis (CASE). The new scale showed excellent interobserver (intraclass correlation coefficient [ICC] = 0.97) and intraobserver (ICC = 0.96) reliability for total scores, was highly correlated with modified Rankin scale (r = 0.86, p
Evidence for the Higgs Boson Decay to a Z Boson and a Photon at the LHC

Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, et al.

Phys Rev Lett, 2024 Jan 12;132(2):021803.
PMID: 38277607 DOI: 10.1103/PhysRevLett.132.021803

The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.