As we suggested in the project proposal, ion-exchange chromatography will be used for the analysis of the contaminants due to optimized asphalt mix. The development of the computer simulation program accelerates and economizes these analyses. Optimization procedure of gradient separations in ion-exchange chromatography using simplex optimization method in combination with the computer simulation program for ion-exchange chromatography is presented. The optimization of parameters describing gradient profile for the separation in ion chromatography is based on the optimization criterion obtained from calculated chromatograms. The optimization criterion depends on the parameters used for calculations and thus exhibits the quality of gradient conditions for the separation of the analytes. Simplex method is used to calculate new gradient profiles in order to reach optimum separations for the selected set of analytes. The Simplex algorithm works stepwise, for each new combination of parameters that describe the gradient profile a new calculation is performed and from the calculated chromatogram the optimization criterion is determined. The proposed method is efficient and may reduce the time and cost of analyses of complex samples with ion-exchange chromatography.

COBISS.SI-ID: 4729626

The optimization of asphalt mixes has to be performed on the basis of experimental design used for the construction of a model for determination of optimal parameters as described in the paper for optimization of pigments. Process optimization involves the minimization (or maximization) of an objective function, that can be established from a technical and (or) economic viewpoint taking into account safety of process. The basic idea of the optimization method using neural network (NN) is to replace the model equations (which traditionally obtained using, for example, the surface response design or others methods) by an equivalent NN. The feed-forward bottleneck neural network (FFBN) as a mapping technique is described and evaluated. From the 2D maps the optimal parameters of pigment dyeing of high performance fibers on the bases of poly-amide benzimidazole (PABI) and polyimide (arimid) are discussed. The studied fibers were treated in 32 experiments under the conditions as proposed by the Design of Experiment (DOE), varying five influencing factors. Neural network mapping method enables visualization of process and shows the influence of different factors on different output responses. Optimum parameters were selected upon compromise decision.

COBISS.SI-ID: 4713754

The carcinogenicity studies of evaporated chemicals during the production of asphalt with new additives are needed for safety reasons. The pre-developed QSAR models help us assess the potential risk of chemicals suspected to emerge into environment during the optimized asphalt production or building-in the roads. The applicability domain (AD) of models developed for regulatory use has attached great attention recently. The AD of quantitative structure-activity relationship (QSAR) models is the response and chemical structure space in which the model makes predictions with a given reliability. The new metrics for the evaluation of the AD of the counter propagation artificial neural network (CP ANN) models are discussed in the article: the Euclidean distances between an object (molecule) and the corresponding excited neuron of the neural network and between an object (molecule) and the representative object (vector of average values of descriptors). The investigation of the training and test sets chemicals coverage in the descriptors space was made with the respect to false predicted chemicals. The leverage approach was used to compare non linear (CP ANN) models with the linear ones.

COBISS.SI-ID: 4861978