Modelling techniques for the control of thermal exchanges in mixed continuous–discontinuous flow food plants

Comba, Lorenzo; Belforte, Guido

doi:10.1016/j.jfoodeng.2011.04.015

Cited by 14 publications

(3 citation statements)

References 27 publications

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“…(Kemény and Ilie-Zudor, 2016; Barge et al, 2017;Soon and Manning, 2019). In some cases, this can be very difficult to implement due to the nature of the product (Barge et al, 2019) or the peculiarity of the operations conducted in the production stages (Comba et al, 2011). This is the case for the example reported in this paper, which considers pork thighs.…”

Section: Introductionmentioning

confidence: 89%

Artificial intelligence to boost traceability systems for fraud prevention in the meat industry

Biglia

Barge²,

Tortia³

et al. 2022

J. Agric. Eng.

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Traceability was introduced about twenty years ago to face the worldwide spread of food safety crises. Traceability data flow associated with each lot of food products during any production and/or delivery phases can also be used to guarantee product authenticity. For this purpose, it is necessary to protect the data from cyber intrusions and, at the same time, to guarantee the integrity of the bond between the physical product and the data. Price grading related to quality perceivable or credence attributes attracts criminals to attempt item substitution fraud. Improved track and trace technologies supported by artificial intelligence (AI) could highly enhance systems’ capability to detect authenticity violations by product substitution. This paper proposes an innovative method based on AI, to reinforce traceability systems in detecting possible counterfeiting by product substitution. It is an item-based mass balance method that analyses the congruity of the traceability data flows not by using explicit (even stochastic) rules but by exploiting the learning capabilities of a neural network. The system can then detect suspect information in a traceability data flow, alerting a possible profit-driven crime. The AI-based method was applied to a pork slaughtering and meat cutting chain case study.

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Section: Introductionmentioning

confidence: 89%

Artificial intelligence to boost traceability systems for fraud prevention in the meat industry

Biglia

Barge²,

Tortia³

et al. 2022

J. Agric. Eng.

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“…Since in the food industry hot water and/or steam are typically required for several thermal processes -e.g. blanching (Xin et al, 2015), cooking, washing, heating (Comba et al, 2010;Comba et al, 2011;Biglia et al, 2015), pasteurisation, debacterisation (Biglia et al, 2017), etc. -the heat released by the working fluid in the HX1 and HX2 may be favourably recovered, as demonstrated and tested in the air cycle prototype developed by Foster el at.…”

Section: Modified Reversed Brayton Cyclementioning

confidence: 99%

Reversed Brayton cycle for food freezing at very low temperatures: Energy performance and optimisation

Biglia

Comba

Fabrizio

et al. 2017

International Journal of Refrigeration

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Freezing is a valuable method to increase food shelf life and to ensure high quality standards during longterm storage. Additional benefits to frozen food quality can be achieved by freezing at very low temperatures (« -50 °C): small ice crystals formation during fast freezing reduces food cell wall rupture, preventing water and texture loss during thawing. This paper presents the design of an innovative food freezing system operating at very low temperatures, based on a modified reversed Brayton cycle (rB cycle). The plant is composed of two interconnected subsystems: a primary thermodynamic closed loop, operated by an rB cycle, and a secondary airflow loop which is devoted to food freezing by batch process. Relevant features of the designed rB cycle rely on the adopted environmentally safe working fluid, the optimised thermodynamics working conditions and the innovative cycle layout. A modelling framework for the system was developed to identify and design efficient operative settings for the plant components (turbo-machineries, heat exchangers, etc.) and to assess, via sensitivity analysis, the influence of the main design parameters on the global energy performance. The proposed system configuration, designed to maximise the Coefficient of Performance (CoP) value of the plant, was determined by means of nonlinear multivariable optimisation. In addition, the energy performance of the system can be increased by recovering waste heat available from the rB cycle.

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“…The management of homogenous lots of products (referred to as cohorts) and of their flow inside the production line is then governed by means compartmental models. This methodology allows to track the composition, in terms of lots of raw material, of any portion of product processed in the plant, and has been previously successfully used in (Comba et al, 2011) to determine precise thermal conditions of fluid product processed in mixed continuous-discontinuous condition (i.e. plants with valves and pumps that can introduce discontinuities in the product flow).…”

Section: Traceability Of Bulk Productsmentioning

confidence: 99%

Open problems in traceability: from raw materials to finished food products

Comba¹,

Dabbene²,

Gay³

et al. 2013

J Agricult Engineer

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Even though the main EU regulations concerning food traceability have already entered to force since many years, we still remark very wide and impacting product recalls, which often involve simultaneously large territories and many countries. This is a clear sign that current traceability procedures and systems, when implemented with the only aim of respecting mandatory policies, are not effective, and that there are some aspects that are at present underestimated, and therefore should be attentively reconsidered. In particular, the sole adoption of the so-called “one step back-one step forward traceability” to comply the EC Regulation 178/2002, where every actor in the chain handles merely the data coming from his supplier and those sent to his client, is in fact not sufficient to control and to limit the impact of a recall action after a risk notification. Recent studies on lots dispersion and routing demonstrate that each stakeholder has to plan his activities (production, transformation or distribution) according to specific criteria that allow pre-emptively estimating and limiting the range action of a possible recall. Moreover, these new and very recently proposed techniques still present some limits; first of all the problem of traceability of bulk products (e.g. liquids, powders, grains, crystals) during production phases that involve mixing operations of several lots of different/same materials. In fact, current traceability practices are in most cases unable to deal efficiently with this kind of products, and, in order to compensate the lack of knowledge about lot composition, typically resort to the adoption of very large lots, based for instance on a considered production period. Aim of this paper is to present recent advances in the design of supply chain traceability systems, discussing problems that are still open and are nowadays subject of research.

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Modelling techniques for the control of thermal exchanges in mixed continuous–discontinuous flow food plants

Cited by 14 publications

References 27 publications

Artificial intelligence to boost traceability systems for fraud prevention in the meat industry

Artificial intelligence to boost traceability systems for fraud prevention in the meat industry

Reversed Brayton cycle for food freezing at very low temperatures: Energy performance and optimisation

Open problems in traceability: from raw materials to finished food products

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