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.

Interactions of microalgae-bacteria consortia for nutrient removal from wastewater: A review.

Nitrogen and phosphorus pollution can cause eutrophication, resulting in ecosystem disruption. Wastewater treatment systems employing microalgae-bacteria consortia have the potential to enhance the nutrient removal efficiency from wastewater through mutual interaction and synergetic effects. The knowledge and control of the mechanisms involved in microalgae-bacteria interaction could improve the system's ability to transform and recover nutrients. In this review, a critical evaluation of recent literature was carried out to synthesize knowledge related to mechanisms of interaction between microalgae and bacteria consortia for nutrient removal from wastewater. It is now established that microalgae can produce oxygen through photosynthesis for bacteria and, in turn, bacteria supply the required metabolites and inorganic carbon source for algae growth. Here we highlight how the interaction between microalgae and bacteria is highly dependent on the nitrogen species in the wastewater. When the nitrogen source is ammonium, the generated oxygen by microalgae has a positive influence on nitrifying bacteria. When the nitrogen source is nitrate, the oxygen can have an inhibitory effect on denitrifying bacteria. However, some strains of microalgae have the capability to supply hydrogen gas for hydrogenotrophic denitrifiers as an energy source. Recent literature on biogranulation of microalgae and bacteria and its application for nutrient removal and biomass recovery is also discussed as a promising approach. Significant research challenges remain for the integration of microalgae-bacteria consortia into wastewater treatment processes including microbial community control and process stability over long time horizons.

Development of Digital Image Processing as an Innovative Method for Activated Sludge Biomass Quantification.

Activated sludge process is the most common method for biological treatment of industrial and municipal wastewater. One of the most important parameters in performance of activated sludge systems is quantitative monitoring of biomass to keep the cell concentration in an optimum range. In this study, a novel method for activated sludge quantification based on image processing and RGB analysis is proposed. According to the results, the intensity of blue color in the macroscopic image of activated sludge culture can be a very accurate index for cell concentration measurement and R2 coefficient, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) which are 0.990, 2.000, 0.323, and 13.848, respectively, prove this claim. Besides, in order to avoid the difficulties of working in the three-parameter space of RGB, converting to grayscale space has been applied which can estimate cell concentration with R 2 = 0.99. Ultimately, an exponential correlation between RGB values and cell concentrations in lower amounts of biomass has been proposed based on Beer-Lambert law which can estimate activated sludge biomass concentration with R 2 = 0.97 based on B index.

Evaluation of various techniques for microalgal biomass quantification.

Biomass concentration is one of the most important parameters in the biotechnology processes. Its measurement relies on the physical, chemical or biological properties of the cells. Several techniques were applied in this work to measure the cell concentration of four microalgae: Botryococcussp., Botryococcusbraunii, Chlorella vulgaris, and Ettlia sp. The experiments were performed using samples taken from a chemostat for each strain to provide microalgal cell suspensions in a stable physiological state and concentration. The dry cell weight (DCW) was used as the reference method for the evaluation of other methods. The two commercial sensors used to determine optical density and dielectric permittivity showed a broad effective measurement range up to more than 20gl(-1). A Red-Green-Blue model analysis of microalgal digital images in combination with Fourier equation significantly extended the measurements range up to 6gl(-1). Cell count using a flow cytometer showed a broad range of linearity to DCW in washed samples, but other counting methods using hemocytometer and microscopic automated count were limited. Finally, the oxygen production rate, representing the photosynthetic activity, showed a linear regression with DCW at cell concentrations lower than 1gl(-1).