The solubility of benzanilide in sixteen pure organic solvents, including N, N-dimethylformamide, tetrahydrofuran, butanone, acetone, ethyl acetate, dichloromethane, n-butanol, diethyl ether, n-propanol, acetonitrile, ethanol, methanol, isobutanol, isopropanol, toluene, and carbon tetrachloride, was measured at temperatures ranging from 274.15 to 313.15 K using the static equilibrium method at atmospheric pressure. The modified Apelblat model, Buchowski-Ksiazczak λh model, and Non Random Two Liquid (NRTL) model were used to correlate the experimental solubility data. It was found that in all these solvents the solubility of benzanilide increases in different pace as the temperature increases. The modified Apelblat model gave the best fit. Guided by the solubility data, preliminary experimental study was conducted for cooling crystallization of benzanilide in ethanol and n-propanol. The result indicated that the solvent, stirring speed and cooling rate all have significant impact on crystal morphology and particle size distribution.

The solubility of 2, 2-azobisisobutyronitrile (AIBN) in pure methanol, ethanol, acetone, benzene, ethyl acetate and a mixture of methanol and water was measured in the temperature range from 268.15 K to 325.15 K at atmospheric pressure. The AIBN solubility was sensitive to the temperature in all the pure solvents. The solubility in the binary mixture of methanol and water increased as the methanol fraction and temperature increased. The data were correlated with the modified Apelblat equation, Van't Hoff equation and Buchowski-Ksiazczak h equation. Other results showed two AIBN polymorphs formed after the crystallization, form I, in a monoclinic cell, and form II, in a triclinic cell, which confirmed the two AIBN crystal structures reported in the literature. Additionally, the initial concentration and cooling rate of the AIBN crystallization had dominant roles in affecting the polymorphic forms of the AIBN crystals. The crystallization conditions were optimized in a 2 L crystallizer to yield crystals with the desired morphology and polymorphs, which resolved the caking issue experienced in the industrial production of AIBN. The optimized conditions were tested in a 20 L crystallizer.

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Current methods for the prediction of mixture toxicity have shown to be valid for mixtures that conform to some assumptions that were ideally formulated for mixtures comprising constituents exhibiting either completely similar or dissimilar mechanisms of action. Approaches are needed that predict the toxicity of mixtures representative of real environmental occurrences i.e., those comprising constituents of mixed similar and dissimilar compounds and therefore are more complex. In this paper such a methodology is proposed which uses molecular descriptors and fuzzy set theory to characterize the degree of similarity and dissimilarity of mixture constituents, integrates the concentration addition and independent action models, and therefore is called INFCIM (INtegrated Fuzzy Concentration addition--Independent action Model). INFCIM is tested in two case studies using toxicity data of four mixtures, and its performance is compared against those of both concentration addition and independent action models. Mixture 1 consists of 18 s-triazines acting on green freshwater algae scenedemus vacuolatus. Mixture 2 comprises 16 acting constituents tested on scenedemus vacuolatus. Both mixtures inhibit reproduction in the biological assays. There are 10 quinolone compounds in mixture 3 and 16 phenol derivative compounds in mixture 4 all causing long-term inhibition of bioluminescence in the marine bacterium Vibrio fischeri. It was shown that INFCIM performed comparably or better than the best performing existing model in the original studies for all the mixtures tested.

Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.

The solubility of dimethyl 2,2′-azobis­(2-methylpropionate) (AIBME) was determined in 15 pure solvents, including cyclohexane, dichloromethane, diethyl ether, acetone, tetrahydrofuran, ethyl acetate, tetrachloromethane, acetonitrile, methylbenzene, methanol, ethanol, n-propyl alcohol, isopropanol, n-butyl alcohol, and iso-butyl alcohol, and in a binary solvent system (methanol + water) at the temperature range from 253.15 to 293.15 K at atmospheric pressure. Between 278.15 and 293.15 K, the solubility of AIBME is more sensitive to temperature in alcohol solvents than in other solvents. However, the solubility of AIBME in nonalcoholic solvents decreased more rapidly than in alcohol solvents below 278.15 K. The AIBME solubility in the binary solvent system (methanol + water) is positive to temperature and the initial mole fraction of methanol. The measured solubility data of AIBME in pure solvents were correlated by the modified Apelblat equation, the λh equation, the nonrandom two-liquid (NRTL) model, and the Wilson model. The measured solubility data of AIBME in binary solvent systems were correlated by the modified Apelblat equation, the nonrandom two-liquid (NRTL) model, and the Wilson model. Among the four models, the NRTL model is the best model to fit the solubility of AIBME, especially in alcohol solvents. In addition, the mixing properties of AIBME in 15 pure solvents, including standard mixing Gibbs energy, mixing enthalpy, and mixing entropy, were calculated and discussed. The results provide useful information for practical optimization of AIBME crystallization processes.

The solubility of 2, 5-Di-Tert-Butylhydroquinone (DTBHQ) in acetone, ethyl acetate, methanol, and the mixtures of ethanol and water was measured and correlated in the temperature range between 278.95 K to 346.15 K. It was found that the solubility in acetone increases more rapidly with increasing temperature than in other solvents. The data was correlated using the modified Apelblat, Buchowski-Ksiazczak h, and Van't Hoff equations, with the maximum relative average deviation less than 2.74 %. The obtained thermodynamic models were used in designing a crystallisation process and synthesizing a flowsheet for effective purification of crude DTBHQ from the by-product of tertiary butylhydroquinone (TBHQ).