novel reactive distillation process for the production of cyclohexanol from cyclohexene

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2010

A determinação dos números de indivíduos mínimos necessários na experimentação genética

The main object of the present paper consists in giving formulas and methods which enable us to determine the minimum number of repetitions or of individuals necessary to garantee some extent the success of an experiment. The theoretical basis of all processes consists essentially in the following. Knowing the frequency of the desired p and of the non desired ovents q we may calculate the frequency of all possi- ble combinations, to be expected in n repetitions, by expanding the binomium (p-+q)n. Determining which of these combinations we want to avoid we calculate their total frequency, selecting the value of the exponent n of the binomium in such a way that this total frequency is equal or smaller than the accepted limit of precision n/pª{ 1/n1 (q/p)n + 1/(n-1)| (q/p)n-1 + 1/ 2!(n-2)| (q/p)n-2 + 1/3(n-3) (q/p)n-3... < Plim - -(1b) There does not exist an absolute limit of precision since its value depends not only upon psychological factors in our judgement, but is at the same sime a function of the number of repetitions For this reasen y have proposed (1,56) two relative values, one equal to 1-5n as the lowest value of probability and the other equal to 1-10n as the highest value of improbability, leaving between them what may be called the "region of doubt However these formulas cannot be applied in our case since this number n is just the unknown quantity. Thus we have to use, instead of the more exact values of these two formulas, the conventional limits of P.lim equal to 0,05 (Precision 5%), equal to 0,01 (Precision 1%, and to 0,001 (Precision P, 1%). The binominal formula as explained above (cf. formula 1, pg. 85), however is of rather limited applicability owing to the excessive calculus necessary, and we have thus to procure approximations as substitutes. We may use, without loss of precision, the following approximations: a) The normal or Gaussean distribution when the expected frequency p has any value between 0,1 and 0,9, and when n is at least superior to ten. b) The Poisson distribution when the expected frequecy p is smaller than 0,1. Tables V to VII show for some special cases that these approximations are very satisfactory. The praticai solution of the following problems, stated in the introduction can now be given: A) What is the minimum number of repititions necessary in order to avoid that any one of a treatments, varieties etc. may be accidentally always the best, on the best and second best, or the first, second, and third best or finally one of the n beat treatments, varieties etc. Using the first term of the binomium, we have the following equation for n: n = log Riim / log (m:) = log Riim / log.m - log a --------------(5) B) What is the minimun number of individuals necessary in 01der that a ceratin type, expected with the frequency p, may appaer at least in one, two, three or a=m+1 individuals. 1) For p between 0,1 and 0,9 and using the Gaussean approximation we have: on - ó. p (1-p) n - a -1.m b= δ. 1-p /p e c = m/p } -------------------(7) n = b + b² + 4 c/ 2 n´ = 1/p n cor = n + n' ---------- (8) We have to use the correction n' when p has a value between 0,25 and 0,75. The greek letters delta represents in the present esse the unilateral limits of the Gaussean distribution for the three conventional limits of precision : 1,64; 2,33; and 3,09 respectively. h we are only interested in having at least one individual, and m becomes equal to zero, the formula reduces to : c= m/p o para a = 1 a = { b + b²}² = b² = δ2 1- p /p }-----------------(9) n = 1/p n (cor) = n + n´ 2) If p is smaller than 0,1 we may use table 1 in order to find the mean m of a Poisson distribution and determine. n = m: p C) Which is the minimun number of individuals necessary for distinguishing two frequencies p1 and p2? 1) When pl and p2 are values between 0,1 and 0,9 we have: n = { δ p1 ( 1-pi) + p2) / p2 (1 - p2) n= 1/p1-p2 }------------ (13) n (cor) We have again to use the unilateral limits of the Gaussean distribution. The correction n' should be used if at least one of the valors pl or p2 has a value between 0,25 and 0,75. A more complicated formula may be used in cases where whe want to increase the precision : n (p1 - p2) δ { p1 (1- p2 ) / n= m δ = δ p1 ( 1 - p1) + p2 ( 1 - p2) c= m / p1 - p2 n = { b2 + 4 4 c }2 }--------- (14) n = 1/ p1 - p2 2) When both pl and p2 are smaller than 0,1 we determine the quocient (pl-r-p2) and procure the corresponding number m2 of a Poisson distribution in table 2. The value n is found by the equation : n = mg /p2 ------------- (15) D) What is the minimun number necessary for distinguishing three or more frequencies, p2 p1 p3. If the frequecies pl p2 p3 are values between 0,1 e 0,9 we have to solve the individual equations and sue the higest value of n thus determined : n 1.2 = {δ p1 (1 - p1) / p1 - p2 }² = Fiim n 1.2 = { δ p1 ( 1 - p1) + p1 ( 1 - p1) }² } -- (16) Delta represents now the bilateral limits of the : Gaussean distrioution : 1,96-2,58-3,29. 2) No table was prepared for the relatively rare cases of a comparison of threes or more frequencies below 0,1 and in such cases extremely high numbers would be required. E) A process is given which serves to solve two problemr of informatory nature : a) if a special type appears in n individuals with a frequency p(obs), what may be the corresponding ideal value of p(esp), or; b) if we study samples of n in diviuals and expect a certain type with a frequency p(esp) what may be the extreme limits of p(obs) in individual farmlies ? I.) If we are dealing with values between 0,1 and 0,9 we may use table 3. To solve the first question we select the respective horizontal line for p(obs) and determine which column corresponds to our value of n and find the respective value of p(esp) by interpolating between columns. In order to solve the second problem we start with the respective column for p(esp) and find the horizontal line for the given value of n either diretly or by approximation and by interpolation. 2) For frequencies smaller than 0,1 we have to use table 4 and transform the fractions p(esp) and p(obs) in numbers of Poisson series by multiplication with n. Tn order to solve the first broblem, we verify in which line the lower Poisson limit is equal to m(obs) and transform the corresponding value of m into frequecy p(esp) by dividing through n. The observed frequency may thus be a chance deviate of any value between 0,0... and the values given by dividing the value of m in the table by n. In the second case we transform first the expectation p(esp) into a value of m and procure in the horizontal line, corresponding to m(esp) the extreme values om m which than must be transformed, by dividing through n into values of p(obs). F) Partial and progressive tests may be recomended in all cases where there is lack of material or where the loss of time is less importent than the cost of large scale experiments since in many cases the minimun number necessary to garantee the results within the limits of precision is rather large. One should not forget that the minimun number really represents at the same time a maximun number, necessary only if one takes into consideration essentially the disfavorable variations, but smaller numbers may frequently already satisfactory results. For instance, by definition, we know that a frequecy of p means that we expect one individual in every total o(f1-p). If there were no chance variations, this number (1- p) will be suficient. and if there were favorable variations a smaller number still may yield one individual of the desired type. r.nus trusting to luck, one may start the experiment with numbers, smaller than the minimun calculated according to the formulas given above, and increase the total untill the desired result is obtained and this may well b ebefore the "minimum number" is reached. Some concrete examples of this partial or progressive procedure are given from our genetical experiments with maize.

Análise de poliembrionia em Citrus, máxime em toranjas

1 - This paper is a joined publication of the Dept. of Genetics, Escola Superior de Agricultura "Luiz de Queiroz", University of São Paulo, and Secção de Citricultura e Frutas Tropicais, Instituto Agronômico, de Campinas, and deal with the number of seed per fruit and the polyembryony in Citrus, with special reference to the pummelos (C. grandis). 2 - For C. pectinifera, hibrid limon x acid lime, C. histrix and Citrus sp. the mean of seeds per fruit is 5,8 - 17,3 - 30,2 -94,6; for 14 pummelos the average was 100 and the range of variation 11 to 185 seeds per fruit. For the four above mentioned Citrus the cotyledons were classified into 3 types: big (near 8 mm.), medium (near 6 mm) and small (near 4 mm) and for the pummelos there was only one size of cotyledons, about 10 mm (table 1). 3 - The polyembryony was determined by two processes: a) counting of the embryos in the mature seed; b) counting after germination in flats or seed-beds. The rasults obtained are in table 2; the process a gave larger results than process b.The following pummelos are monoembryonics: melancia, inerme, Kaune Paune, sunshine, vermelha, Singapura, periforme, Zamboa, doce, Indochina, Lau-Tau, Shantenyau and Siamesa. Sometime it was found a branching of the main stem that gave a impression of polyembryonic seeds. 4 - It was shown by the x2 test that the distribution of embryo numbers fits the Poisson's series (table 2) in both processes. 5 - It is discussed in table 2 the variability of polyembryony for the following cases: a) between plants, within years. The teste for the differences of mean of polyembryony between 3 plants of C. pectinifera is statistically significant in 1948 and 1949; b) between yields of the same plant, within year. The same case of C. pectinifera may be used for this purpose; c) between process, within year. It is shown in table 3, for C. pectinifera and the hibrid "limon x acid lime" that there is a statistically signicicant between both process above mentioned.

Influence of water quality and kind of metal in the secondary cooling zone of casting process

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2012

Collaboration process design for ideation in distributed environments : approaches to support collaborative ideation in global virtual groups using technological support

Magdeburg, Univ., Fak. für Informatik, Diss., 2012

Algorithmic assistance to the optimization process in vehicle routing problems

Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2013

Issues of information support for the process of scientific enterprise's management in the universities and research organizations

This article describes the problem of commercializing of scientific researches in universities. Management tasks are reduced to subtasks and combined formal algorithm. The overall control problem is reduced to a set of formal subtasks combined into a single algorithm. Here the necessity of joint control of all commercialization projects as well as the use of information systems for the successful implementation of the existing commercialpotential is shown.

Electron Beam Welding In-Process Control and Monitoring

This work presents the results of an investigation of processes in the melting zone during Electron Beam Welding(EBW) through analysis of the secondary current in the plasma.The studies show that the spectrum of the secondary emission signal during steel welding has a pronounced periodic component at a frequency of around 15–25 kHz. The signal contains quasi-periodic sharp peaks (impulses). These impulses have stochastically varying amplitude and follow each other inseries, at random intervals between series. The impulses have a considerable current (up to 0.5 A). It was established that during electron-beam welding with the focal spot scanning these impulses follow each other almost periodically. It was shown that the probability of occurrence of these high-frequency perturbation increases with the concentration of energy in the interaction zone. The paper also presents hypotheses for the mechanism of the formation of the high-frequency oscillations in the secondary current signal in the plasma.

Designing crystallization based-enantiomeric separation for chiral compound-forming systems in consideration of polymorphism and solvate formation

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Process design based on CO 2-expanded liquids as solvents

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Evaluation of process concepts for liquid-liquid systems exemplified for the indirect hydration of cyclohexene to cyclohexanol

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Process of fixation, imobilization and mineralization of ammonium in soil using N-15

The organic and inorganic forms of soil nitrogen and how they participate in the process of fixation, immobilization and mineralization of ammonium in soils were evaluated, after different periods of incubaton, utilizing two soils, a Lithic Haplustoll and a Typic Eutrorthox. The results obtained permit to suggest that : 1) The method for determination of the ammonium fixing capacity based on the extraction with 2N KC1, is considered to be subject to interferences of other soil fractions capable of retaining ammonium. 2) The increase in exchangeable ammonium content is related to the decrease in amino acids and hydrolyzable ammonium. 3) The immobilization and mineralization processes are still held under mil microbial. The forms more affected by this condition are amino acids and hydrolyzable ammonium.

Does bounded rationality lead to individual heterogeneity? The impact of the experimentation process and of memory constraints

In this paper we explore the effect of bounded rationality on the convergence of individual behavior toward equilibrium. In the context of a Cournot game with a unique and symmetric Nash equilibrium, firms are modeled as adaptive economic agents through a genetic algorithm. Computational experiments show that (1) there is remarkable heterogeneity across identical but boundedly rational agents; (2) such individual heterogeneity is not simply a consequence of the random elements contained in the genetic algorithm; (3) the more rational agents are in terms of memory abilities and pre-play evaluation of strategies, the less heterogeneous they are in their actions. At the limit case of full rationality, the outcome converges to the standard result of uniform individual behavior.