Comprehensive review of water management and wastewater treatment in food processing industries in the framework of water-food-environment nexus

Food processing is among the greatest water-consuming industries with a significant role in the implementation of sustainable development goals. Water-consuming industries such as food processing have become a threat to limited freshwater resources, and numerous attempts are being carried out in order to develop and apply novel approaches for water management in these industries. Studies have shown the positive impact of the new methods of process integration (e.g., water pinch, mathematical optimization, etc.) in maximizing water reuse and recycle. Applying these methods in food processing industries not only significantly supported water consumption minimization but also contributed to environmental protection by reducing wastewater generation. The methods can also increase the productivity of these industries and direct them to sustainable production. This interconnection led to a new subcategory in nexus studies known as water-food-environment nexus. The nexus assures sustainable food production with minimum freshwater consumption and minimizes the environmental destructions caused by untreated wastewater discharge. The aim of this study was to provide a thorough review of water-food-environment nexus application in food processing industries and explore the nexus from different aspects. The current study explored the process of food industries in different sectors regarding water consumption and wastewater generation, both qualitatively and quantitatively. The most recent wastewater treatment methods carried out in different food processing sectors were also reviewed. This review provided a comprehensive literature for choosing the optimum scenario of water and wastewater management in food processing industries.     

Extraction of ferulic acid from sugar beet pulp by alkaline hydrolysis and organic solvent methods

Extraction of ferulic acid from sugar beet pulp was carried out using three extraction solvents, sodium hydroxide (0.5, 1, 2 M), methanol and their mixture (alkaline methanolic solvent). The Ferulic acid extracted by each solvent was identified and quantified by HPLC method and the effects of solvent type, concentration and reaction time on ferulic acid solubilisation were assessed. There were differences in the contents of products extracted in the experiment conditions. The minimum amount of ferulic acid was obtained from methanolic extract while the highest concentrations (957.4 mg/L ferulic acid) were obtained employing the highest NaOH concentration (2 M), and reaction time (12 h), so phenolic compounds are better released with alkaline hydrolysis than in methanol conditions. Finally a simple procedure for the purification of ferulic acid from the alkaline extracts is presented and evaluated by FT-IR spectrum.     

Melt Production and Antimicrobial Efficiency of Low-Density Polyethylene (LDPE)-Silver Nanocomposite Film

Colloidal silver nanoparticles with a size of 5.5 ± 1.1 nm were prepared by chemical reduction using polyethylene glycol (PEG). Silver nanoparticles were incorporated into low-density polyethylene (LDPE) by melt blending and subsequent hot pressing at 140 °C to produce nanocomposite film with an average thickness of 0.7 mm. PEG was added at 5% weight of polymer as a compatibilizer agent in order to prevent agglomeration and provide uniform distribution of nanoparticles in polymer matrix. Antimicrobial activity of silver nanocomposites against Escherichia coli ATCC 13706, Staphylococcus aureus ATCC12600, and Candida albicans ATCC10231 was evaluated by semi-qualitative agar diffusion test and quantitative dynamic shake flask test. Mechanical properties of nanocomposites were not significantly different from silver-free LDPE-containing PEG films (p > 0.05), and silver nanoparticles did not form chemical bonding with the polymer. LDPE-silver nanocomposite samples by more than 6.69 ppm silver nanoparticles showed considerable antimicrobial clear zone. LDPE-silver nanocomposite affected growth kinetic parameters of the examined bacteria and is more efficient on S. aureus than E. coli. Polyethylene-silver nanocomposites containing 22.64 ppm silver nanoparticles could reduce 57.8% growth rate and 23.3% maximum bacterial concentration and increase 35.8% lag time of S. aureus. This study shows the potential use of LDPE-silver nanocomposite as antimicrobial active film. Antimicrobial efficiency of silver nanocomposite depends on silver nanoparticles concentration; however, high level of silver nanoparticles may lead to weakening of mechanical properties.     

Double Auction-based Scheduling of Scientific Applications in Distributed Grid and Cloud Environments

Economy models have long been considered as a promising complement to the classical distributed resource management not only due of their dynamic and decentralized nature, but also because the concept of financial valuation of resources and services is an inherent part of any such model. In its broadest sense, scheduling of scientific applications in distributed Grid and Cloud environments can be regarded as a market-based negotiation between a scheduling service optimizing user-centric objectives (execution time, budget), and a resource manager optimizing provider-centric metrics (resource utilization, income, job throughput). In this paper, we propose a new instantiation of the negotiation protocol between the scheduler and resource manager using a market-based Continuous Double Auction (CDA) model. We analyze different scheduling strategies that can be applied and identify general strategic patterns that can lead to a fast and cheap work ow execution. In the experimental study, we demonstrate that under certain circumstances one can benefit by applying an aggressive scheduling strategy.     

Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.     

Complementary‐based coalition formation for energy microgrids

In recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.     

Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.