Finding correlations between hop catalogs and real hoppy character in brewery conditions

Brewing recipe design is mainly based on brewer’s expertise, information available in catalogs and certificates of analysis (CoA’s). Hop schedule design and formulation has become an essential topic since hoppy craft beers took the scene. But how accurate is the flavor profile information provided in catalogs? How useful is the chemical composition profile information in CoA’s? Besides current research and tons of reported experiences, hops impact is still a mystery, and topics like biotransformation are black-boxes for brewers. In this study, nine single hopped beers were brewed, and a trained panel conducted sensorial beer analysis. Then, to asses hop impact, qualitative and process-related-quantitative beer characteristics were contrasted to find valuable correlations and trends between hop catalogs and final beers. Discrepancies with catalog qualitative data were reported. In addition to what is already described in the literature, here we describe how α-acids, linalool, myrcene, and geraniol (despite the classical use for these compounds) could predict positive and negative hop impact of nine different hop varieties on bitterness, flavor, and aroma, when they are applied in different brewing process steps. Also, with this pipeline we stand the basis of a tool to be improved, available for brewers, to better predict their brews and assess new hop varieties in real-life pilot brewing set ups.

SCHEDULING TECHNIQUE OF ROUTE VEHICLES ON DUPLICATING STRETCHES

Schedule optimization is a proven strategy to improve service quality for public transport networks. However, current research mostly optimizes schedule design using prior knowledge of users’ routings, ignoring the optimization of public transport schedules on duplicating stretches of route vehicles. This article presents a new alignment technique of time intervals between consecutive vehicles of different routes on duplicating stretches, considering existing public transport networks in attaining optimization of public transport schedule. Scheduling technique of route vehicles on duplicating stretches includes some steps: analysis of public transport network and determining a lot of duplicating stretches, calculation of the optimal time intervals among arrivals of route vehicles and alignment of these intervals among consecutive route vehicles on duplicating stretches, realization simulation model of urban passenger transport within the simulation modeling system of GPSS World. Furthermore, testing the optimization technique of route vehicle scheduling, analysis of the quality of adjusted schedule with route vehicles of different kinds included, and determining the optimization efficiency for duplicating stretches. The adjusting technique of urban passenger transport schedule allows to increase movement steadiness of consecutive vehicles of different routes on duplicating stretches, adjust traffic intervals for each route, shorten the traffic load on stations, reducing idle time and queue lengths of route vehicles in front of transport stops and also minimize waiting time for route vehicle by those passengers who can be transported along several routes. Improving the bus schedule on duplicating stretches in Gomel is illustrated demonstrating the technique developed. This scheduling technique can be used by carriers and transport operators to improve the quality of services provided.

Evaluation of urban water networks - Case study

The subject of the paper is the elaboration of the topic of modeling and optimization of the reliability of systems for drinking water supply in the town of Hlucin and the adjacent villages Bobrovniky and Darkovicky. The paper points out modern ways of management and maintenance of these buildings, eg in the form of passportization, unification of various types of documentation, records of failures and accidents, etc. The aim is to point out possible ways to optimize these buildings and overall evaluation of the water supply network, including the suitable renewal schedule design. These practices are essential for the efficient operation of water supply networks, especially nowadays, when there is a shortage of water and it is necessary to promote sustainable urban development through its economical management.

Nonpreemptive Goal Programing Method in Optimization Nurse Scheduling by Considering Education Level

Nurse scheduling is one of the problems that often arise in hospital management systems. Head of ICU room and nurse to cooperate in making good nurse scheduling for the creation of optimal service. In this paper, we study a hospital nurse schedule design by considering the level of nurse education and the provision of holidays. Nurses with undergraduate education (S1) Nurses become leaders on every shift and are accompanied by nurses with diploma education (D3). The scheduling model in this study using the nonpreemptive goal programming method and LINGO 11.0 software. The preparation of the schedule of nurses assigned to this method can optimize the need for efficient nurses per shift based on education level. The data in the research was obtained by collecting administrative data at Aloei Saboe Gorontalo hospital. The data used are the published schedule by the head of the ICU room. In making a nurse schedule, there are limitations to consider such ashospital regulation. The results of the study obtained an optimal solution in the form of meeting all the desired obstacles. Computational results shows that nurse scheduling using the nonpreemptive goal programming method and LINGO 11.0 software better than the schedule created manually. Every shift is a maximum of one leader with an undergraduate education (S1) background and accompanied by a nurse with a diploma education (D3) background. Keywords: scheduling, goal programming, nonpreemptive goal programming.

Successful Pass Schedule Design in Open-Die Forging Using Double Deep Q-Learning

In order to not only produce an open-die forged part with the desired final geometry but to also maintain economic production, precise process planning is necessary. However, due to the incremental forming of the billet, often with several hundred strokes, the process design is arbitrarily complicated and, even today, often only based on experience or simple mathematical models describing the geometry development. Hence, in this paper, fast process models were merged with a double deep Q-learning algorithm to enable a pass schedule design including multi-objective optimization. The presented implementation of a double deep Q-learning algorithm was successfully trained on an industrial-scale forging process and converged stable against high reward values. The generated pass schedules reliably produced the desired final ingot geometry, utilized the available press force well without exceeding plant limits, and, at the same time, minimized the number of passes. Finally, a forging experiment was performed at the institute of metal forming to validate the generated results. Overall, a proof of concept for the pass schedule design in open-die forging via double deep Q-learning was achieved which opens various starting points for future work.