Soluble gas stabilization (SGS) is an emerging preservation technology in which food products are pre-saturated with carbon dioxide (CO2) prior to packaging to exploit its bacteriostatic effects. The effectiveness of SGS is governed by CO2 diffusion within the food matrix; however, this diffusivity cannot be directly measured and is typically estimated through time-consuming experiments combined with empirical modeling. In this study, a predictive numerical model was developed to estimate the effective diffusion of CO2 in food tissues, treated as a porous medium. The porous geometry was derived from histological sections of pre-rigor salmon loin, where CO2 transport occurs through the liquid phase within both extracellular pores and muscle fibers. The model accounts for variations in food structure and composition and predicts effective diffusivity based on the more controllable diffusivity of CO2 in water, thereby reducing experimental uncertainty. Model predictions showed strong agreement with experimental data. No statistically significant temperature effect on CO2 diffusivity was observed between 1 degrees C and 4 degrees C at an average initial pressure of 160 kPa (rho = 0.451). Comparative analysis with literature data acquired under comparable operating conditions further confirmed that effective diffusivity is influenced by the gas-to-product volume ratio and diffusion distance. At equal diffusion distances, higher gas-to-product volume ratios resulted in increased diffusivity due to sustained higher headspace pressures and enhanced mass transfer. Greater diffusion depths were also associated with higher effective diffusivity, potentially linked to interfacial convection effects. Overall, the proposed framework provides a robust and standardized approach for optimizing the SGS process while reducing experimental effort and facilitating its industrial implementation.

Sustainable food production along with food security and safety demands attention. Reducing the undiagnosed impacts of the food processing sector contributes to the transition towards a more sustainable food production system. Consequently, food processing technologies and production planning should be developed or modified with caution to align with sustainability issues. Appropriate tools are needed to ensure the complete coverage of different aspects of sustainability in the design phase and to recognize the opportunities for sustainability improvements in the use phase. This study proposes a structured tool to analyze the sustainability of food processing technologies from the stakeholder’s points of view, that can be used to make more knowledgeable decisions and find manageable trade-offs. The proposed tool is adapted from the acknowledged Sustainable Development Analytical Grid (SDAG) tool. The theoretical contribution of this study is the synthesis of literature to identify sustainability criteria for integrating into the design phase, thereby enhancing sustainability across the entire life cycle. A case study from the food sector illustrates the applicability of the tool and suggests solutions to address the identified sustainability issues. Future research should strengthen the validity and applicability of the proposed tool through additional cases.

This study focuses on the chemical, physical, and biological hazards that pose food contamination risks during the processing of food in facilities using open food processing equipment through a review of published literature from 2015 to 2023. Ten main pathways for food contamination were developed and a list of chemical, physical, and biological food hazards, along with descriptions of process parameters and inputs that can contribute to food contamination, and prevention strategies associated with each pathway were compiled. The paper briefly discusses the relation between food contamination and the sustainable development goals (SDGs). The presented overview of contamination pathways and their associated food hazards can provide insights for food safety management plans, food processing equipment design, food processing facility layout, HACCP programs, and further studies on hygienic monitoring methods.

Product Lifecycle Management (PLM) plays a key role in digital transformation demanded by Industry 4.0 and life cycle assessment, including sustainability assessment. Knowledge Based Engineering (KBE) applications can support PLM by integrating heterogeneous knowledge from different stages throughout the product life. However, the integration of knowledge from different stages and teams can cause misunderstanding if not represented in a unified form. Furthermore, different forms of knowledge used by different software are neither machine-readable nor human-readable, which also sets obstacles to knowledge integration in KBE applications. Supply chain sustainability assessment is such a scenario that entails integrating knowledge from different sources. This paper firstly implements a sustainability assessment method from other scholar to calculate the supply chain sustainability performance and adapts a sustainability assessment ontology for supply chain sustainability assessment. Then, an example KBE application is developed by implementing the sustainability assessment ontology and calculation method to simulate the knowledge sharing and integration between different teams. Finally, through this example application, it is discussed that the implementation of ontology to represent knowledge in PLM application for collaborative tasks like sustainability assessment can increase the efficiency of data sharing and integration. This paper is a proof of concept for the ontology-based framework. This framework can facilitate to represent knowledge but not create new knowledge, which means it can increase the efficiency of the software development, but cannot provide a better calculation method and assessment framework for supply chain sustainability assessment.

Critical function prototyping (CFP) is useful when developing product service systems. Focusing on testing the critical component of the system first, as opposed to the whole system, saves resources that can be used elsewhere. This paper exemplifies CFP applied to a drone-based cleaning system, where the cleaning process is considered the critical function. Three CFPs were built and tested to find the most promising design. The best design was then tested to further explore the feasibility of the critical function. The method enabled us to test fast, decreasing the risk while saving resources.

Over the last decades, the intense need for more robust and lightweight structures, together with the dramatic improvement of computational power, had, as a result, the introduction of simulations in the traditional product development. As a simulation, it is considered any computer process that imitates a real system by generating similar responses over time. Simulations allow the designers to create virtual prototypes that can speed up the design phase and, thus, the product development time in total. This design paradigm shift is called simulation-based design (SBD) and includes several simulations and optimization techniques. The most notable of these techniques are; computer-aided design (CAD), finite element analysis (FEA), topology optimization (TO), and parametric optimization (PO). A combined SBD methodology, including these techniques, is presented here. This methodology is a two-stage optimization process. During the first stage, traditional compliance TO using the SIMP approach was conducted, while at the second, a PO with an evolutionary algorithm was applied. The presented methodology is focused on the optimization of composite laminates. In particular, an angle-ply laminated beam made by carbon fiber reinforced polymer (FRP) was used as a case study and optimized both for its topology and fibers’ direction. The results of this research are presented and tested using a commercial example. The suggested methodology resulted in a lighter and more robust design solution. These design solutions can be constructed either by conventional manufacturing processes (CMP) or by additive manufacturing (AM). Designers looking for interesting and lightweight composited structures can exploit the results found in this paper. The implemented process can easily be modified in order to cover any possible optimization of FRP products.

Background: Increasing the shelf life of perishable food products contributes to lower food waste and the possibility of widening distribution outreach in the food value chain. Soluble gas stabilization (SGS) technology is a pre-step process of dissolving carbon dioxide (CO2) into the product before packaging. This technology shows promising results on the lab-scale to limit microbial growth and other deteriorating mechanisms in food products. Scope and approach: This review aims to gather available research results on the effects of combining SGS technology or dissolved CO2 with thermal and non-thermal processing technologies. The effects are structured according to the microbiological shelf life and safety as well as food quality parameters such as texture, color, drip loss, lipid oxidation, and adenosine triphosphate (ATP) degradation. This paper reviews the SGS effects alone and in combination with conventional food treatments on the parameters mentioned above. Key findings and conclusions: Improving thermal and non-thermal technologies efficacy meets the demand for better food quality while being more economically feasible. Combining dissolved CO2 with these treatments, as hurdle technology, considerably enhances the bacteriostatic effect of the treatments, mostly without compromising the product quality. However, it is highly dependent on the product kind, treatment method, experiment protocol, and composition and concentration of the product microbiota. Moreover, the extent of positive synergistic effects could be promoted by addressing specific problems such as gas layer formation during sous vide treatment. This paper provides a better understanding of the SGS effectiveness, performing beside conventional food processing technologies, for the full-scale implementation of the technology.

Biofouling is a serious problem in marine aquaculture and it has a number of negative impacts including increased forces on aquaculture structures and reduced water exchange across nets. This in turn affects the behavior of fish cages in waves and currents and has an impact on the water volume and quality inside net pens. Even though these negative effects are acknowledged by the research community and governmental institutions, there is limited knowledge about fouling related effects on the flow past nets, and more detailed investigations distinguishing between different fouling types have been called for. This study evaluates the effect of hydroids, an important fouling organism in Norwegian aquaculture, on the forces acting on net panels. Drag forces on clean and fouled nets were measured in a flume tank, and net solidity including effect of fouling were determined using image analysis. The relationship between net solidity and drag was assessed, and it was found that a solidity increase due to hydroids caused less additional drag than a similar increase caused by change in clean net parameters. For solidities tested in this study, the difference in drag force increase could be as high as 43% between fouled and clean nets with same solidity. The relationship between solidity and drag force is well described by exponential functions for clean as well as for fouled nets. A method is proposed to parameterize the effect of fouling in terms of an increase in net solidity. This allows existing numerical methods developed for clean nets to be used to model the effects of biofouling on nets. Measurements with other types of fouling can be added to build a database on effects of the accumulation of different fouling organisms on aquaculture nets.

Pancreas disease (PD) is a viral disease causing negative impacts on economy of salmon farms and fish welfare. Its transmission route is horizontal, and water transport by ocean currents is an important factor for transmission. In this study, the effect of temperature changes on PD dynamics in the field has been analysed for the first time. To identify the potential time of exposure to the virus causing PD, a hydrodynamic current model was used. A cohort of salmon was assumed to be infected the month it was exposed to virus from other infective cohorts by estimated water contact. The number of months from exposure to outbreak defined the incubation period, which was used in this investigation to explore the relationship between temperature changes and PD dynamics. The time of outbreak was identified by peak in mortality based on monthly records from active sites. Survival analysis demonstrated that cohorts exposed to virus at decreasing sea temperature had a significantly longer incubation period than cohorts infected when the sea temperature was increasing. Hydrodynamic models can provide information on the risk of being exposed to pathogens from neighbouring farms. With the knowledge of temperature-dependent outbreak probability, the farmers can emphasize prophylactic management, avoid stressful operations until the sea temperature is decreasing and consider removal of cohorts at risk, if possible.

Salting is one of the basic procedures in food processing. The NaCl concentration influences water holding properties, viscosity, texture, emulsification, etc. It is important to study and model mixing processes by mathematical methods to predict properties of the matrix and the food quality. The objective of this study was to develop a mathematical model of mixing of salt and meat in a bowl cutter and verify this with experimental data. The bowl cutter is described as a continuous stirred tank reactor, combined with a plug flow reactor in a repetitive series-model. The theoretical model shows that 30 rounds are sufficient to get a salt concentration in the whole bulk of meat with a deviation between maximum and minimum values of about 5%. The comparison of the theoretically predicted salt gradient and the experimental results showed that the mathematical equation developed is appropriate to describe the process. (C) 2012 Elsevier Ltd. All rights reserved.

Cod bite on aquaculture net cages has resulted in damages like frayed netting and holes, which in part can explain why cultured cod have escaped more frequently than salmon over the last years. We describe damages found on various netting materials subjected to cod bite through field experiments at commercial cod farms. Further, a method to test local cod bite resistance of traditional netting structures is suggested and initial results from a test jig prototype are given. Results from field experiments indicated that cod may have been attracted by types of netting that made it possible to draw filaments out of the twine, while stiff, coated netting structures and thick filaments showed no sign of bite damage during the test period. We concluded that netting materials for cod aquaculture must be resistant to cod bite or be repellent or uninteresting for cod. Based on the present findings, the better choice among the traditional netting materials seemed to be hard-laid netting materials, preferably with a primer that glues the filaments together.

One of the possibilities to expand sea-based fish farming is to move the aquaculture installations away from the conflicts of the coastal zone, and into more open ocean locations. However, open ocean aquaculture puts other demands on the structures than aquaculture in sheltered locations, and in this context it is necessary to understand the behaviour of the aquaculture structures as they are exposed to large sea-loads from waves and current. Flexible netting is a main part of most sea-based aquaculture structures, and in this paper the interaction between waves and netting is studied. Experiments were conducted at the narrow wave flume facility at the University of Oslo, Norway, where several different regular wave cases were run through netting with different solidity. The wave energy was measured after the wave had passed through the net and compared with the energy of an undisturbed wave to assess the wave damping properties of the net. The vertical and horizontal forces were also measured. The findings show that the damping effects of the netting are not necessarily correlated with the wave forces, indicated complex nonlinear processes contributing to the fluid-net interaction. The amount of nonlinear energy in the wave and force waveforms is also investigated, and it is shown that the nonlinear energy in the incoming wave results in an even higher level of nonlinear components in the forces experienced by the net.

An iterative method for estimating the failure probability for certain time-variant reliability problems has been developed. In the paper, the focus is on the displacement response of a linear oscillator driven by white noise. Failure is then assumed to occur when the displacement response exceeds a critical threshold. The iteration procedure is a two-step method. On the first iteration, a simple control function promoting failure is constructed using the design point weighting principle. After time discretization, two points are chosen to construct a compound deterministic control function. It is based on the time point when the first maximum of the homogenous solution has occurred and on the point at the end of the considered time interval. An importance sampling technique is used in order to estimate the failure probability functional on a set of initial values of state space variables and time. On the second iteration, the concept of optimal control function can be implemented to construct a Markov control which allows much better accuracy in the failure probability estimate than the simple control function. On both iterations, the concept of changing the probability measure by the Girsanov transformation is utilized. As a result the CPU time is substantially reduced compared with the crude Monte Carlo procedure.

In order to perform a strength analysis of a net cage, it is crucial to know the tensile stiffness properties of the netting material. A new method for testing was established in order to determine the tensile properties of knotless netting materials. We applied it to a variety of netting materials and developed stress–strain relations. The stiffness was expressed as a constant value for relatively small strains, while for large strains the stress–strain relation was defined by a third degree polynomial. The average value of the constant stiffness for the tested wet netting materials was 81Nmm−2 with a standard deviation of 9Nmm−2 for strains less than 10%. For netting materials treated with anti-fouling paint, the average constant stiffness value was 131Nmm−2 with a standard deviation of 13Nmm−2 for strains less than 30%. The results are valid for uniaxial static loading of netting.

One of the possibilities to expand sea-based fish farming is to move aquaculture installations away from the conflicts of the coastal zone, and into more open ocean locations. However, open ocean aquaculture places different demands on structures than aquaculture in sheltered locations. In this context, it is necessary to understand the behaviour of the aquaculture structures as they are exposed to large sea-loads from waves and current. Flexible netting is the main part of most sea-based aquaculture structures, and in this paper the interaction between waves and netting is studied. Experiments were conducted at the narrow wave flume facility at the University of Oslo, Norway, where several different regular wave cases were run through netting with different solidity. The wave geometry was measured on each side of the net to determine how the wave changed as it moved trough the net. In particular, the damping effect of the net on the passing wave was analysed, but also more subtle features, such as crest steepness and asymmetry factors where studied.

The main objective of this thesis is to develop an efficient simulation technique to estimate the failure probability of time-dependent systems, whose state is expressed as a solution of Ito stoch ...

An iterative method for estimating the failure probability for time-dependent reliability problems has been developed. The system response has been modeled by a diffusion process, solution of an Ito stochastic differential equation. On the first iteration a simple control function has been built using a design point weighting principle for a reliability problem. After time discretization, two points were chosen to construct the compound deterministic control function. It is based on the time point when the first maximum of the homogenous solution has occurred and on the end point of the considered time interval. An importance sampling technique is used in order to estimate the failure probability functional on a set of initial values of state space variables and time. On the second iteration, the concept of optimal control function developed by Milstein has been implemented to construct a Markov control which provides better accuracy of the failure probability estimator than the simple control function. On both iterations, a concept of changing the probability measure by the Girsanov transformation is utilized. As a result the lower variance of estimates is achieved by fewer samples and the CPU time is reduced by order of 10 compared with the crude Monte Carlo procedure.