The effects of the independent variables, milk homogenization pressure (p1), concentration factor of milk microfiltration (i) and pH on the rheological properties of rennet milk gels were studied. Nondestructive oscillatory rheometry was used to determine rennet coagulation time (RCT), curd firming rate (CFR) and cutting time (CT). A central composite design, comprising two levels of i (1 and 2), pH (6.4 and 6.6) and p1 (0 and 8 MPa), was applied. Second‐order polynomial models successfully described (R2 > 0.92) the relationship between processing parameters and rheological properties of the gels. pH had the most important influence on RCT, while CFR and CT were strongly influenced by i, pH and the interaction of i and pH. In contradiction to studies on active filler interactions for acid milk gels, a discrepancy was observed between results obtained by compression test and rheometry. Rennet gel firmness strongly decreased with a rise in p1 when measured using the compression test, whereby CFR increased with an increase in p1 when measured using rheometry. The latter result corresponds to higher storage modulus values after a certain time indicating higher gel stiffness. This effect was stronger for concentrated milk than for unconcentrated milk. PRACTICAL APPLICATIONS The use of microfiltration (MF) and homogenization may reduce raw material and processing time in conventional cheese manufacture. However, MF markedly influences milk composition, and homogenization alters the particle size distribution of fat globules. Hence, both technologies may influence rennet‐induced gel formation, syneresis, cheese composition and quality. Curd firmness of homogenized milk is often too weak to resist the extensive curd treatment applied in semi‐hard cheese manufacture which causes loss of curd fines during the syneresis process and finally decreases cheese yield. MF leads to high curd firmness if cutting is not performed at the appropriate time, which unnecessarily extends processing time. The study of the effect of the individual treatments, as well as of the combination of both on rennet‐induced gel formation, is the first important step to evaluate their impact on further processing steps in cheese making. The combination of both technologies may overcome the antagonistic effect of the individual technology as low curd firmness due to homogenization can be compensated by MF that increases curd firmness and vice versa.
Railway network operations form complex systems. Any disruption adversely impacts the operations, causing long delays. Many studies investigate the effect of a railway incident; however, a holistic quantification is lacking. This study aims to present an estimation framework for flexible traffic management systems, which can help reduce network delays and enable dispatchers to make better-informed decisions. An incident's impact on the network is estimated by creating a sequence of models, which predict two key variables. Firstly, the incident duration is predicted, which is used to predict the second variable: total delay caused by the incident. Various influencing attributes are examined, such as weather, network and railway-related attributes. Their relationship with the response variables is studied in order to understand the incident's impact. Using incident data from the Danish Railways, machine learning models are estimated. The results show that neural networks outperform other competing models for total delay modelling, resulting in improved prediction by the estimation framework, thus giving higher accuracy than the stand-alone models in the study. INTRODUCTIONReal-time operations of a railway system are unavoidably subject to incidents and disruptions, resulting in delays and influences the timetables. As the duration of an incident increases, the impact on the network varies, leading to potential cumulative delays. Recovering from a disrupted situation requires timetable changes, and in some cases, changes in the rolling stock and crew rosters as well [1]. Currently, there is a rising need to quantify the impacts of disruptions [2]. Incidents are critical and need to trigger an immediate response, as a small delay could quickly propagate into the network, disrupting many connections and causing more delays, compromising customer service [3]. In railway traffic management systems (RTMS), any response to the occurrence of an incident aims at the fastest possible recovery of the system in failure [4]. Most of the literature is based on traditional railway systems that are usually microscopic. Microscopic approachesThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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