Application of a genetic algorithm based model selection algorithm for identification of carbide-based hot metal desulfurization

Abstract Sulfur is considered as one of the main impurities in hot metal. Hot metal desulfurization is often carried out with pneumatic injection of a fine-grade desulfurization reagent using a submerged lance. The aim of this study was to develop a data-driven model for the process. The model selection algorithm carries out a simultaneous variable selection and optimization of number of hidden neurons with a combination of binary and integer coded Genetic Algorithm. The objective function applied in the search is repeated Leave-Multiple-Out cross-validation. The model considered is a feedforward neural network with a single hidden layer. In the inner loop of the algorithm, the computational load is reduced by making use of Extreme Learning Machine (ELM) architecture. The final model is trained using the Bayesian regularization. The results show that a well-generalizing data-driven model with good prediction performance can be repeatedly selected based on noisy industrial data with the help of a Genetic Algorithm, provided that the model is validated comprehensively with internal and external data sets

Comparison of single control loop performance monitoring methods

Abstract Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback–Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes

Oxide scale formation of EN 1.4622 and EN 1.4828 stainless steels during annealing and descaling behavior in neutral electrolytic pickling

Abstract Oxide scale formation during short-term annealing and electrolytic pickling behavior of ferritic EN 1.4622 and austenitic EN 1.4828 stainless steels are investigated. The annealing is performed at temperatures between 1000 and 1100 °C for ferritic and 1100 and 1200 °C for austenitic steel grade under humid atmospheres in simulated industrial process. Neutral electrolytic pickling, also referred to as neutral electrochemical pickling or the Ruthner Neolyte Process, is performed in Na₂SO₄ electrolyte, and pickling efficiency is evaluated visually and by image analysis of pickled surfaces. The results show that annealing conditions have a more impactful effect on the structure and the composition of the resulting oxide in the austenitic grade within the studied condition range. The thicknesses of the ferritic scales are mainly less than 0.4 μm, while almost all austenitic scales are thicker than it. In addition, the amount of silicon oxide formation inside the steel matrix of the austenitic and ferritic grades is highly different. Longer exposure times and higher temperatures promote scale growth during annealing, resulting in inefficient electrolytic pickling for the ferritic grade. For the austenitic grade, almost all steel surfaces are still covered with oxide scale after electrolytic pickling

Effect of simulated annealing conditions on scale formation and neutral electrolytic pickling

Abstract Scale formation of AISI 304 stainless steel during annealing at temperatures between 1100 and 1200 °C under a water vapor‐containing atmosphere is studied. Characterization of the oxide scale is performed with field‐emission scanning electron microscopy–energy dispersive spectroscopy (FESEM–EDS) and glow discharge optical emission spectroscopy (GDOES) and removal of oxide scale is done via neutral electrolyte pickling. The pickling conditions are kept constant and the effect of the annealing conditions and scale properties on the pickling result are examined. The effectiveness of pickling is evaluated using analysis FESEM images taken on polished sections of pickled surfaces. Research shows that the thickness, morphology, and composition of the oxide scale are dependent on annealing temperature and time. The thicknesses of the scale formed under the established conditions vary from 0.2 to over 30 μm, and morphologies between the chromium rich oxide layer and layered scale structure formed by breakaway oxidation. The pickling response of oxide scales remains good at all annealing temperatures with the shortest exposure time

The effect of laparoscopic technique on the surgical outcome of colorectal cancer in a small-volume rural Finnish Lapland Central Hospital

Abstract Introduction: Laparoscopic colorectal surgery has become widely used in treating colorectal cancer. Multicenter studies have shown that laparoscopy decreases postoperative complications and provides equivalent long-term oncological results compared to open surgery. Previous studies were conducted in high-volume institutions, with selected patients, which may influence the reported outcome of laparoscopy. Methods: All patients with colorectal cancer that underwent surgery for a primary tumor between 2005 and 2015 in the Lapland Central Hospital were retrospectively collected. We retrieved data on the primary surgical outcome and complications within the first 30 days after surgery from patient records. We surveyed the national patient registry to determine long-term oncological results and patient survival. Results: We identified 349 patients treated for colorectal cancer during 2005–2015. Of these, 219 patients (median age 71 years) underwent laparoscopy and 130 (median age 72 years) underwent open surgery. The 5-year disease-specific survival rates for stages I–III colon cancer were 83.3 and 87.7%, respectively. The 3-year disease-specific survival rates for stages I–III rectal cancer were 86.1 and 65.0%, respectively. Conclusions: Our results showed that the introduction of laparoscopic colorectal surgery for treating cancer in a rural, small-volume hospital provided short- and long-term results comparable to findings from previous studies conducted in high-volume centers. Therefore, laparoscopy should be considered the treatment of choice for colorectal cancer in small, rural clinics

Data-driven mathematical modeling of the effect of particle size distribution on the transitory reaction kinetics of hot metal desulfurization

Abstract The aim of this work was to develop a prediction model for hot metal desulfurization. More specifically, the study aimed at finding a set of explanatory variables that are mandatory in prediction of the kinetics of the lime-based transitory desulfurization reaction and evolution of the sulfur content in the hot metal. The prediction models were built through multivariable analysis of process data and phenomena-based simulations. The model parameters for the suggested model types are identified by solving multivariable least-squares cost functions with suitable solution strategies. One conclusion we arrived at was that in order to accurately predict the rate of desulfurization, it is necessary to know the particle size distribution of the desulfurization reagent. It was also observed that a genetic algorithm can be successfully applied in numerical parameter identification of the proposed model type. It was found that even a very simplistic parameterized expression for the first-order rate constant provides more accurate prediction for the end content of sulfur compared to more complex models, if the data set applied for the modeling contains the adequate information