IMPACT OF GRAPE POMACE MACERATION ON THE QUALITY OF RHINE RIESLING WINE

Vina Rajnskog rizlinga proizvedena standardnom tehnologijom za bijela vina te maceracijom masulja od 3 sata i maceracijom masulja od 6 sati analizirana su kako bi se utvrdile razlike u kemijskom sastavu i senzornim svojstvima. Kemijska analiza provedena je nakon fermentacije, a senzorno ocjenjivanje 3 mjeseca nakon fermentacije. Vina dobivena maceracijom masulja od 3 sata senzorno su ocijenjena najbolje dok su vina dobivena maceracijom od 6 sati te standardnom tehnologijom bila slabije kakvoće. Po kemijskom sastavu vina dobivena maceracijom masulja nisu se međusobno razlikovala, dok su vina dobivena standardnim postupkom proizvodnje za bijela vina imala niži ukupni ekstrakt, pepeo, hlapivu kiselost i ukupne fenole.Rhine riesling wines produced by usual technology for white wines and by maceration for 3 hours and maceration for 6 hours were investigated for differences in chemical composition and sensory properties. Chemical analyses were performed after fermentation and sensory testing was done 3 months after fermentation. Wine produced by maceration for 3 hours was evaluated as sensory the best while the wine produced by maceration for 6 hours and usual technology was of inferior quality. In chemical composition there was no difference between wine produced by maceration of grape pomace while wines produced by standard technology for white wine had lower total extract, ashes, volatyle acidity and total phenols

Genetic Algorithm to Evolve Ensembles of Rules for On-Line Scheduling on Single Machine with Variable Capacity

International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC (8th . 2019. Almería, Spain

Genetic programming for job shop scheduling

© 2019, Springer International Publishing AG, part of Springer Nature. Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies