A comparative study between LS-SVM method and semi empirical equations for modeling the solubility of different solutes in supercritical carbon dioxide

A genetic algorithm based least square support vector machine has been used to predict the solubility of 25 different solutes in supercritical carbon dioxide. This model consists of three inputs including temperature, pressure and density of supercritical carbon dioxide and a single output which is the solubility of different solutes in supercritical carbon dioxide. The model predictions were compared with the outputs of seven well-known semi empirical correlations. Results showed that the present method has an average relative deviation of about 4.92% for 25 solutes while the best semi empirical equation resulted an average relative deviation of about 13.60% for same solutes.     

A round robin study for the characterization of latex particle morphology—multiple analytical techniques to probe specific structural features

Determining the detailed morphology of composite latex particles in a confident manner is often very challenging and sometimes seemingly impossible. This paper reports on the details of an interlaboratory study, a so-called ‘round robin’ study, designed to determine the complete details of the particle structure of a particular styrene-acrylic copolymer latex system. Independent organizations received portions of the same composite latex and each performed several analytical measurements of the characteristics of the latex particles. Techniques included SEM, TEM, AFM, NMR, DSC, MFFT, GPC, CHDF and QELS. Each analytical test was performed by at least two independent laboratories using the protocols adopted by the individual organizations. Subsequently representatives from each of the six laboratories participated in a workshop to contribute their data, assess the results from all of the information developed, and to draw collective conclusions regarding the detailed structure of the particles. Complete morphology details must include the overall particle shape, the composition of its outermost region, and the internal structure. Multiple, complimentary sets of analytical data were necessary to confidently determine the particle structure, even for the well phase separated latex particles studied here.     

State of health estimation of lithium‐ion batteries: A multiscale Gaussian process regression modeling approach

Accurate state of health (SOH) estimation in lithium‐ion batteries, which plays a significant role not only in state of charge (SOC) estimation but also in remaining useful life (RUL) prognostics is studied. SOC estimation and RUL prognostics often require one‐step‐ahead and long‐term SOH prediction, respectively. A systematic multiscale Gaussian process regression (GPR) modeling method is proposed to tackle accurate SOH estimation problems. Wavelet analysis method is utilized to decouple global degradation, local regeneration and fluctuations in SOH time series. GPR with the inclusion of time index is utilized to fit the extracted global degradation trend, and GPR with the input of lag vector is designed to recursively predict local regeneration and fluctuations. The proposed method is validated through experimental data from lithium‐ion batteries degradation test. Both one‐step‐ahead and multi‐step‐ahead SOH prediction performances are thoroughly evaluated. The satisfactory results illustrate that the proposed method outperform GPR models without trend extraction. It is thus indicated that the proposed multiscale GPR modeling method can not only be greatly helpful to both RUL prognostics and SOC estimation for lithium‐ion batteries, but also provide a general promising approach to tackle complex time series prediction in health management systems. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1589–1600, 2015     

Application of chemometrics to study the kinetics of coal pyrolysis: A novel approach

In present investigation, chemometric tools, principal component analysis (PCA) and Hierarchical clustering analysis (HCA) are used to get the linkage between the coal properties and kinetics of pyrolysis. Thermo gravimetric analysis (TGA) of 10 perhydrous Indian coals was done. Devolatilization of these coals showed five independent reactions. Kinetic parameters were calculated for individual reaction. Activation energy and weight loss of each reaction has been analyzed as a function of coal properties (moisture, volatile matter, ash, fixed carbon, carbon, hydrogen, nitrogen and sulfur). By applying chemometric, was extracted information about the linkage between activation energies of each reaction and coal properties. The mathematical treatment of data has provided conclusions on properties of coal and kinetic parameters.     

Anionic polymerization of lactams: A comparative study on various methods of measuring the conversion of ε‐caprolactam to polyamide 6

Anionic ring‐opening polymerization of lactams leads to the formation of poly(lactams) or polyamides. This work aimed at comparing the performance of four methods for measuring the conversion of ε‐caprolactam (CL) to polyamide 6. The latter was either a homopolymer (PA6) or grafts onto polystyrene (PS‐g‐PA6 graft copolymer). Those four methods were mass balance based on solvent extraction (methanol, water, THF, or acetone), mass balance based on vacuum drying at 140°C, thermogravimetric analysis (TGA), and elemental analysis based on nitrogen. The mass balances based on methanol extraction and vacuum drying at 140°C and TGA were all suitable for measuring the conversion of CL, whether the resulting polymer was the PA6 or PS‐g‐PA6. The mass balance based on water extraction was good for the PA6 and not good for the PS‐g‐PA6. The elemental analysis based on nitrogen was not suitable for the PA6 nor for the PS‐g‐PA6. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 1972–1981, 2006