Neural networks and genetic algorithm as robust optimization tools for modeling the microbial production of poly‐β‐hydroxybutyrate (PHB) from Brewers’ spent grain

Imandi, Sarat Babu; Karanam, Sita Kumari; Nagumantri, Radhakrishna; Srivastava, Rajesh Kumar; Sarangi, Prakash Kumar

doi:10.1002/bab.2412

Cited by 8 publications

(6 citation statements)

References 82 publications

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“…ANNs operate directly on input-output data, so there must be a sufficient number of data to train the ANN and produce valid results [20]. In this study, a comprehensive dataset consisting of 384 samples was utilized to develop and evaluate the proposed models and optimization methods for the mashing process in triticale wort production.…”

Section: Dataset Analysismentioning

confidence: 99%

“…GAs mimic the biology of chromosome generation with operators such as mutation, crossover, and the selection criteria of the random population of individuals. By applying an objective function, each individual is evaluated based on a fitness criterion [20].…”

Section: Introductionmentioning

confidence: 99%

“…For that reason, an ANN and a support vector machine were applied to predict acetic acid content at the end of commercial-scale beer fermentation [23]. ANNs and GAs were not only applied in the brewing process, but also to optimize the further utilization of brewing bio-waste materials such as brewers' spent grain [20], where the biosynthesis of poly-β hydroxybutyrate from brewers' spent grain was modeled and optimized using an ANN-GA strategy. Besides that, better control of a brewery's waste water was achieved by developing a robust mathematical tool for performance prediction using an ANN [24].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm

Pribić,

Kamenko,

Despotović

et al. 2024

Foods

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Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production.

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Section: Dataset Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm

Pribić,

Kamenko,

Despotović

et al. 2024

Foods

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“…These microorganisms can grow on synthetic media or carbonrich by-products, such as food-processing waste streams. Two strategies are commonly used during brewery waste processing: pure culture [95,114] and mixed-microbial culture (MMC) [96,115]. Amini and co-workers produced PHA from BWW with an added carbon source (maltose) with the commonly used Cupriavidus necator strain.…”

Section: Pha and Related Biopolymersmentioning

confidence: 99%

By-Product Valorization as a Means for the Brewing Industry to Move toward a Circular Bioeconomy

Pasquet,

Villain-Gambier,

Trébouet

2024

Sustainability

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Beer production consumes significant amounts of water, energy, and raw materials, and results in the production of various by-products, including wastewater, brewers’ spent grain, yeast and hop. To lower its environmental footprint, by-products may be reclaimed or valorized in agro-food, cosmetic, material, chemical industries, etc. According to most recent research, breweries have the potential to become biorefineries, as they can extract diverse valuable plant-based compounds such as carbohydrates, proteins, lipids, phenolic compounds, platform chemicals, and biopolymers. These biomolecules possess bioactive and physicochemical properties, which can be enhanced through recovery processes. Brewery by-products may be utilized in various industries within the bioeconomy frame. In agro-food systems, extracts can increase final products’ techno-functionalities. Such additives can also help in creating marketing labels such as clean-label healthy, which can further attract potential customers. Businesses can gain economic and socio-environmental benefits by implementing sustainable practices, which can also improve their corporate image. This article outlines recent advancements in the processing and valorization of brewery by-products, ultimately defining an up-to-date, sustainable strategy for clean beer production.

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“…Industrial applications of BSG not only leverage BSG as a valuable animal feedstock but also as a bioreactor input for PHA synthesis [7]. Numerous studies have focused on optimizing microbial PHA production using BSG as a substrate, demonstrating the feasibility of utilizing this waste product for sustainable bioplastic production [8].…”

Section: Introductionmentioning

confidence: 99%

Harnessing Brewery Spent Grain for Polyhydroxyalkanoate Production

Terfa,

Patel,

Kim

et al. 2024

Macromol

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The utility of brewery spent grain (BSG), a byproduct of the beer production process, for the synthesis of polyhydroxyalkanoates (PHAs), is a significant advancement towards sustainable and cost-effective biopolymer production. This paper reviews the upcycling potential of BSG as a substrate for PHA production, utilizing various biotechnological approaches to convert this abundant waste material into high-value biodegradable polymers. Through a comprehensive review of recent studies, we highlight the biochemical composition of BSG and its suitability for microbial fermentation processes. This research delves into different methodologies for PHA production from BSG, including the use of mixed microbial cultures (MMCs) for the synthesis of volatile fatty acids (VFAs), a critical precursor in PHA production, and solid-state fermentation (SSF) techniques. We also examine the optimization of process parameters such as pH, temperature, and microbial concentration through the application of the Doehlert design, revealing the intricate relationships between these factors and their impact on VFA profiles and PHA yields. Additionally, this paper discusses challenges and future perspectives for enhancing the efficiency and economic viability of PHA production from BSG. By harnessing the untapped potential of BSG, this research contributes to the development of a circular economy model, emphasizing waste valorization and the creation of sustainable alternatives to conventional plastics.

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Neural networks and genetic algorithm as robust optimization tools for modeling the microbial production of poly‐β‐hydroxybutyrate (PHB) from Brewers’ spent grain

Cited by 8 publications

References 82 publications

Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm

Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm

By-Product Valorization as a Means for the Brewing Industry to Move toward a Circular Bioeconomy

Harnessing Brewery Spent Grain for Polyhydroxyalkanoate Production

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