مجله پژوهشهای علوم و صنایع غذایی ایران (Dec 2023)
Evaluation of the Importance of Multi-objective Particle Swarm Algorithm Parameters in Optimizing the Solutes Rejection of Camel Milk Ultrafiltration Using Partial Least Squares Regression
Abstract
Introduction Ultrafiltration is one of the most common membrane processes in the dairy industry, especially for condensing and separating milk components. Using this process, several products can be produced, including milk concentrate used for cheese production, low-lactose dairy products, milk protein concentrate, and serum proteins for dietary supplements. The efficiency and cost of a membrane process depend on the percentage of rejection of the soluble components. Therefore, the use of concentrated milk made by ultrafiltration in the production of various dairy products depends on the efficiency of the membrane process and the changes in milk components during this process. On the one hand, the physicochemical properties of camel milk are different from those of cow milk, especially in terms of type and amount of protein. Because significant differences exist between the physicochemical properties of camel and cow milk, likely, the membrane processing conditions and the physicochemical properties of their products will be different completely. Although many studies have been conducted on the efficacy of the ultrafiltration processing of cow milk, there is no information about the efficacy of camel milk ultrafiltration, and most of the research done regarding optimizing is based on classical algorithms, Therefore, in this study, the effects of transmembrane pressure and temperature on the solutes rejection (protein, lactose, ash, and total solids) during camel milk ultrafiltration process were investigated, Then, these properties were optimized using particle swarm algorithm. Also, because the performance of the particle swarm algorithm is highly dependent on related parameters such as the number of iterations, the number of particles, accelerate constant, inertia weight, and velocity of the particles, so before optimization, the effect of these parameters on optimal responses were examined by partial least squares regression (PLS). Materials and Methods In this study, a pilot crossflow ultrafiltration system was used. A UF membrane (Model 3838 HFK-131, Koch membrane systems, Inc., USA) made of polysulfone amid (PSA) with MWCO of 20 kDa was applied. Camel milk was purchased from a local market in Mashhad and for camel skim milk production, its fat was separated by a pilot plant milk fat separator in the Food Research Complex, Ferdowsi University of Mashhad. The weight percentages of protein, fat, lactose, ash, and total solids of UF permeate samples were measured by ISO 8968-1:2014, ISO 1211: 2010, ISO 26462/IDF 214:2010, ISO 5544:2008, and ISO 6731:2010 at two replications, respectively. the process treatments were performed in the form of a central composite design (CCD) (5 replications at the central point) for two independent variables at three levels so that the total number of 13 treatments was obtained. The data were modeled using the statistical software of Design Expert (version 11) based on the response surface methodology and each of the response variables in the form of a regression model was presented as a function of independent variables. Results and Discussion The rejection of total solids and protein of the tested samples varied in the range of 45.4-51.03% and 94.09-97.51%, respectively. It means that in each TMP and T, more than 45% of the total solids and 94% of the protein of camel milk were kept by the membrane. The results also showed that none of the linear, quadratic and interactive effects of TMP and T on the total solids and protein rejections were not significant. According to the results, the RL reduced with increasing T. Increasing the TMP also led to a reduction at high T and an increase in RL rate of the samples at lover T. Also, the effect of TMP on RA showed a non-linear trend, so that TMP at high T led to an increase, and at low T, it led to a reduction in the RA of the samples. Conclusion The optimization results with the particle swarm algorithm showed that this algorithm has a high convergence speed and by recognizing and analyzing its parameters, the optimal conditions can be easily found. The optimum ultrafiltration conditions in this study with the lowest RL and RA were determined as 80 kPa TMP and 29.85 ͦ C T.
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