Kinetic models for biomass pyrolysis

Biomass can be thermally treated to generate a wide range of valuable products, which can be used as a fuel or for chemicals production. Pyrolysis is a popular thermal process that is used to transform biomass into either bio-oil or bio-char by controlling the operating conditions (e.g., temperature and residence time); however, because biomass is a highly heterogeneous material, its pyrolysis involves complex chemical and physical changes. Various proposed mechanisms have been successful in capturing different aspects of biomass pyrolysis in different conditions, but there is still lack of consensus on a definitive kinetic mechanism. This review summarizes and discusses different types of kinetic models used to describe biomass pyrolysis and predict product yield

Kinetic analysis of biomass pyrolysis with a peak temperature method

The aim of this study is to develop a straightforward method to perform a kinetic analysis of the biomass pyrolysis, applying a developed peak temperature method to the experimental thermogravimetric data. When performing a non-linear fit to the experimental data, initial values of the parameters to fit, are required. With this peak temperature method, the initial values can be estimated by direct observation of the differential thermogravimetric curves (DTG), which should nott present any difficulty and it will help to achieve convergence more quickly

Silica-supported quinolinium tribromide: a recoverable solid brominating reagent for regioselective monobromination of aromatic amines

Silica-supported quinolinium tribromide was synthesized and found to be an efficient, stable, and recoverable solid brominating reagent for the regioselective monobromination of aromatic amines. This protocol has advantages of high yield, mild condition and simple work-up procedure

Release of alkali metals during biomass thermal conversion

Biomass has great potential to become an economic source of renewable energy; however, its high chlorine and alkali metal content may cause series problems (e.g. slagging and corrosion) thus limiting its utilization. This paper reviews the release of potassium during biomass thermal conversion. Organic potassium is released first when the temperature is relatively low, starting at about 473 K and slowing down at about 773 K; the release of inorganic potassium occurs with the increase of processing temperature. The potassium vapors are mainly in the form of KCl, KOH and K 2 SO 4 . In addition to the temperature, the properties of biomass feedstock, fuel-air ratio, pressure and heating rate also significantly influence the release rate of alkali metals. Future studies are required to develop accurate kinetic models of potassium release to address the ash related challenges when firing and co-firing biomass with high inherent alkali content

Die analyse eines mathematischen Modells und der Regressionsgleichung für die berechnung inerziäller Parameter von Körperteilen in jungen Chinesinnen

Objective: In this study a mathematical model was set up for calculating the inertial parameters of body segments in young Chinese female students. Methods: On the sample of 50 young Chinese women the inertial parameters, mass and mass center of body segments, were determined by using the Computed Tomography – Digital Image Processing (CT-DIP) method. Results: A 16-segment mathematical model of young Chinese women was set up and a binary regression equation for inertial parameters of body segments calculation was established, in which body weight and stature were treated as independent variables. Conclusion: The study provided a method for a simple calculation of mass, mass centre and moment of inertia both of the segments and of the total body in the population of young Chinese womenUvod Od samih početaka proučavanja čovječjeg kretanja, koncept fizikalnog modeliranja tijela bio je imanentan tim istraživanjima. Braune i Fischer, Hanavan, Hatze, Delp – autori su koji su obilježili razvoj modeliranja ljudskog tijela s primjenama u kineziologiji. Ovaj rad temelji se na pristupu Millerove i Nelsona, odnosno Zatsiorskyog i Seluyanova. Cilj je bio odrediti i analizirati inercijske podatke tijela mladih kineskih žena. Metode Uzorak je obuhvatio 50 žena, studentica, u dobi od 18 do 23 godine. Uporabljena je metoda Computed Tomography – Digital Image Processing (CT-DIP) za analizu 16 segmenata tijela. Na temelju razine sive boje i gustoće svake strukture i tkiva u slikama tih segmenata, inercijski parametri segmenata izracunati su metodom konačnih jedinica. Na kraju su procijenjeni masa, središte mase i moment inercije segmenta. Matematički model je opisan kao n-segmentni sustav s više stupnjeva slobode na spojevima segmenata. To je sustav sastavljen od niza pretpostavljeno homogenih krutih tijela jednostavnih geometrijskih oblika koji su spojeni u zglobovima, a na temelju Hanavanova koncepta 16-segmentnog sustava. Utvrđena je binarna regresijska jednadžba kojom se može izračunati masa, središte mase i moment inercije tjelesnih segmenata. Tjelesna masa i visina korištene su kao nezavisne varijable. Rezultati Prikazani su prosječni podaci za masu, središte mase i moment inercije segmenta i cijelog tijela izračunati metodom CT-DIP. Prikazani su kompletni rezultati dizajna 16- segmentnog modela tijela i inercijalni parametri izračunati binarnom regresijskom jednadžbom. Rasprava Uporabom balansne ploče i metode CT-DIP određeni su i uspoređeni inercijski podaci tijela. Pored standardnih nalaza, autori konstatiraju da ovakav pristup odražava različite karakteristike različitih ljudskih rasa. U okviru evaluacije binarne regresijske jednadžbe najveće diskrepancije u rezultatima pojavile su se u rezultatima za šake i stopala. Međutim, te diskrepancije nisu ozbiljnije utjecale na izračune jer su njihovi momenti bili izuzetno maleni u usporedbi s onima ostalih segmenata. U usporedbi izračunatih vrijednosti i onih dobivenih na balansnoj platformi najveća apsolutna varijanca mase cijeloga tijela iznosila je 3.01 kg, najveća relativna varijacija 3.88%, najveća apsolutna varijanca središta mase cijelog tijela 3.78 cm i najveća relativna varijacija 3.75%. Zaključak Provedena studija pokazala je da je pristup matematičkim modeliranjem dobro prilagođen populaciji mladih kineskih žena. Binarna regresijska jednadžba pokazala se jednostavnim, a vrijednim sredstvom u primjeni tog postupka. Potrebna su daljnja istraživanja da bi se povećala pouzdanost zaključaka.Ziel: In dieser Studie wurde ein mathematisches Modell für die Berechunng inerziäller Parameter von Hörperteilen in jungen Chinesinnen gestaltet. Methoden: Inerziälle Parameter, die Masse und das Massenzentrum von Hörperteilen wurden bei 50 jungen Chinesinnen mittels der Digitalen Bildbearbeitungsmethode (Digital Image Processing – DIP) bestimmt. Ergebnisse: 16-teiliges mathematisches Modell von jungen Chinesinnen wurde gestaltet zusammen mit der binären Regressionsgleichung für die Berechnung von inerziällen Parametern des Hörpergewichts und der Hörperteile. Hörpergewicht und Statur wurden als unabhängige Variablen betrachtet. Schlussfolgerung: Das Resultat dieser Analyse war die Gestaltung einer Methode für die einfache Berechnung von Masse, Massenzentrum und Inerzmoment einzelner Hörperteile und des ganzen Hörpers bei jungen Chinesinnen

The periodic points of ε-contractive maps in fuzzy metric spaces

[EN] In this paper, we introduce the notion of ε-contractive maps in fuzzy metric space (X, M, ∗) and study the periodicities of ε-contractive maps. We show that if (X, M, ∗) is compact and f : X −→ X is ε-contractive, then P(f) = ∩ ∞n=1f n (X) and each connected component of X contains at most one periodic point of f, where P(f) is the set of periodic points of f. Furthermore, we present two examples to illustrate the applicability of the obtained results.Project supported by NNSF of China (11761011) and NSF of Guangxi (2020GXNSFAA297010) and PYMRBAP for Guangxi CU(2021KY0651)Sun, T.; Han, C.; Su, G.; Qin, B.; Li, L. (2021). The periodic points of ε-contractive maps in fuzzy metric spaces. Applied General Topology. 22(2):311-319. https://doi.org/10.4995/agt.2021.14449OJS311319222M. Abbas, M. Imdad and D. Gopal, ψ-weak contractions in fuzzy metric spaces, Iranian J. Fuzzy Syst. 8 (2011), 141-148.I. Beg, C. Vetro, D, Gopal and M. Imdad, (Φ, ψ)-weak contractions in intuitionistic fuzzy metric spaces, J. Intel. Fuzzy Syst. 26 (2014), 2497-2504. https://doi.org/10.3233/IFS-130920A. George and P. Veeramani, On some results in fuzzy metric spaces, Fuzzy Sets Syst. 64 (1994), 395-399. https://doi.org/10.1016/0165-0114(94)90162-7M. Grabiec, Fixed points in fuzzy metric spaces, Fuzzy Sets Syst. 27 (1989), 385-389. https://doi.org/10.1016/0165-0114(88)90064-4V. Gregori and J. J. Miñana, Some remarks on fuzzy contractive mappings, Fuzzy Sets Syst. 251 (2014), 101-103. https://doi.org/10.1016/j.fss.2014.01.002V. Gregori and J. J. Miñana, On fuzzy $Psi$-contractive sequences and fixed point theorems, Fuzzy Sets Syst. 300 (2016), 93-101. https://doi.org/10.1016/j.fss.2015.12.010V. Gregori and A. Sapena, On fixed-point theorems in fuzzy metric spaces, Fuzzy Sets Syst. 125 (2002), 245-252. https://doi.org/10.1016/S0165-0114(00)00088-9J. Harjani, B. López and K. Sadarangani, Fixed point theorems for cyclic weak contractions in compact metric spaces, J. Nonl. Sci. Appl. 6 (2013), 279-284. https://doi.org/10.22436/jnsa.006.04.05X. Hu, Z. Mo and Y. Zhen, On compactnesses of fuzzy metric spaces (Chinese), J. Sichuan Norm. Univer. (Natur. Sei.) 32 (2009), 184-187.I. Kramosil and J. Michàlek, Fuzzy metrics and statistical metric spaces, Kybernetika 11 (1975), 336-344.D. Mihet, Fuzzy ψ-contractive mappings in non-Archimedean fuzzy metric spaces, Fuzzy Sets Sys. 159 (2008), 739-744. https://doi.org/10.1016/j.fss.2007.07.006D. Mihet, A note on fuzzy contractive mappings in fuzzy metric spaces, Fuzzy Sets Syst. 251 (2014), 83-91. https://doi.org/10.1016/j.fss.2014.04.010B. Schweizer and A. Sklar, Statistical metrics paces, Pacif. J. Math. 10 (1960), 385-389. https://doi.org/10.2140/pjm.1960.10.313Y. Shen, D. Qiu and W. Chen, Fixed point theorems in fuzzy metric spaces, Appl. Math. Letters 25 (2012), 138-141. https://doi.org/10.1016/j.aml.2011.08.002S. Shukla, D. Gopal and A. F. Roldán-López-de-Hierro, Some fixed point theorems in 1-M-complete fuzzy metric-like spaces, Inter. J. General Syst. 45 (2016), 815-829. https://doi.org/10.1080/03081079.2016.1153084S. Shukla, D. Gopal and W. Sintunavarat, A new class of fuzzy contractive mappings and fixed point theorems, Fuzzy Sets Syst. 359 (2018), 85-94. https://doi.org/10.1016/j.fss.2018.02.010D. Wardowski, Fuzzy contractive mappings and fixed points in fuzzy metric spaces, Fuzzy Sets Syst. 222 (2013), 108-114. https://doi.org/10.1016/j.fss.2013.01.012D. Zheng and P. Wang, On probabilistic Ψ-contractions in Menger probabilistic metric spaces, Fuzzy Sets Syst. 350 (2018), 107-110. https://doi.org/10.1016/j.fss.2018.02.011D. Zheng and P. Wang, Meir-Keeler theorems in fuzzy metric spaces, Fuzzy Sets Syst. 370 (2019), 120-128. https://doi.org/10.1016/j.fss.2018.08.01

The depth and the attracting centre for a continuous map on a fuzzy metric interval

[EN] Let I be a fuzzy metric interval and f be a continuous map from I to I. Denote by R(f), Ω(f) and ω(x, f) the set of recurrent points of f, the set of non-wandering points of f and the set of ω- limit points of x under f, respectively. Write ω(f) = ∪x∈Iω(x, f), ωn+1(f) = ∪x∈ωn(f)ω(x, f) and Ωn+1(f) = Ω(f|Ωn(f)) for any positive integer n. In this paper, we show that Ω2(f) = R(f) and the depth of f is at most 2, and ω3(f) = ω2(f) and the depth of the attracting centre of f is at most 2.Project supported by NNSF of China (11761011, 71862003) and NSF of Guangxi (2018GXNSFAA294010) and SF of Guangxi University of Finance and Economics (2019QNB10).Sun, T.; Li, L.; Su, G.; Han, C.; Xia, G. (2020). The depth and the attracting centre for a continuous map on a fuzzy metric interval. Applied General Topology. 21(2):285-294. https://doi.org/10.4995/agt.2020.13126OJS285294212A. George and P. Veeramani, On some results in fuzzy metric spaces, Fuzzy Sets Sys. 64 (1994), 395-399. https://doi.org/10.1016/0165-0114(94)90162-7M. Grabiec, Fixed points in fuzzy metric spaces, Fuzzy Sets Sys. 27 (1989), 385-389. https://doi.org/10.1016/0165-0114(88)90064-4V. Gregori and J. J. Miñana, Some remarks on fuzzy contractive mappings, Fuzzy Sets Sys. 251 (2014), 101-103. https://doi.org/10.1016/j.fss.2014.01.002V. Gregori and J. J. Miñana, On fuzzy Ψ-contractive sequences and fixed point theorems, Fuzzy Sets Sys. 300 (2016), 93-101. https://doi.org/10.1016/j.fss.2015.12.010V. Gregori and A. Sapena, On fixed-point theorems in fuzzy metric spaces, Fuzzy Sets Sys. 125 (2002), 245-252. https://doi.org/10.1016/S0165-0114(00)00088-9X. Hu, Z. Mo and Y. Zhen, On compactnesses of fuzzy metric spaces (Chinese), J. Sichuan Norm. Univer. (Natur. Sei.) 32 (2009), 184-187.I. Kramosil and J. Michalek, Fuzzy metrics and statistical metric spaces, Kybernetika 11 (1975), 336-344.C. Li and Y. Zhang, On connectedness of the Hausdorff fuzzy metric spaces, Italian J. Pure Appl. Math. 42 (2019), 458-466.D. Mihet, Fuzzy Ψ-contractive mappings in non-Archimedean fuzzy metric spaces, Fuzzy Sets Sys. 159 (2008), 739-744. https://doi.org/10.1016/j.fss.2007.07.006D. Mihet, A note on fuzzy contractive mappings in fuzzy metric spaces, Fuzzy Sets Sys. 251 (2014), 83-91. https://doi.org/10.1016/j.fss.2014.04.010J. Rodríguez-López and S. Romaguera, The Hausdorff fuzzy metric on compact sets, Fuzzy Sets Sys. 147 (2004), 273-283. https://doi.org/10.1016/j.fss.2003.09.007B. Schweizer and A. Sklar, Statistical metrics paces, Pacif. J. Math. 10 (1960), 385-389. https://doi.org/10.2140/pjm.1960.10.313Y. Shen, D. Qiu and W. Chen, Fixed point theorems in fuzzy metric spaces, Appl. Math. Letters 25 (2012), 138-141. https://doi.org/10.1016/j.aml.2011.08.002D. Wardowski, Fuzzy contractive mappings and fixed points in fuzzy metric spaces, Fuzzy Sets Sys. 222 (2013), 108-114. https://doi.org/10.1016/j.fss.2013.01.012D. Zheng and P. Wang, On probabilistic Ψ-contractions in Menger probabilistic metric spaces, Fuzzy Sets Sys. 350 (2018), 107-110. https://doi.org/10.1016/j.fss.2018.02.011D. Zheng and P. Wang, Meir-Keeler theorems in fuzzy metric spaces, Fuzzy Sets Sys. 370 (2019), 120-128. https://doi.org/10.1016/j.fss.2018.08.01

Empirical evaluations of analytical issues arising from predicting HLA alleles using multiple SNPs

BACKGROUND: Numerous immune-mediated diseases have been associated with the class I and II HLA genes located within the major histocompatibility complex (MHC) consisting of highly polymorphic alleles encoded by the HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1 loci. Genotyping for HLA alleles is complex and relatively expensive. Recent studies have demonstrated the feasibility of predicting HLA alleles, using MHC SNPs inside and outside of HLA that are typically included in SNP arrays and are commonly available in genome-wide association studies (GWAS). We have recently described a novel method that is complementary to the previous methods, for accurately predicting HLA alleles using unphased flanking SNPs genotypes. In this manuscript, we address several practical issues relevant to the application of this methodology. RESULTS: Applying this new methodology to three large independent study cohorts, we have evaluated the performance of the predictive models in ethnically diverse populations. Specifically, we have found that utilizing imputed in addition to genotyped SNPs generally yields comparable if not better performance in prediction accuracies. Our evaluation also supports the idea that predictive models trained on one population are transferable to other populations of the same ethnicity. Further, when the training set includes multi-ethnic populations, the resulting models are reliable and perform well for the same subpopulations across all HLA genes. In contrast, the predictive models built from single ethnic populations have superior performance within the same ethnic population, but are not likely to perform well in other ethnic populations. CONCLUSIONS: The empirical explorations reported here provide further evidence in support of the application of this approach for predicting HLA alleles with GWAS-derived SNP data. Utilizing all available samples, we have built "state of the art" predictive models for HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1. The HLA allele predictive models, along with the program used to carry out the prediction, are available on our website