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.     

Bioactive compounds, antioxidant, xanthine oxidase inhibitory, tyrosinase inhibitory and anti-inflammatory activities of selected agro-industrial by-products

Evaluation of abundantly available agro-industrial by-products for their bioactive compounds and biological activities is beneficial in particular for the food and pharmaceutical industries. In this study, rapeseed meal, cottonseed meal and soybean meal were investigated for the presence of bioactive compounds and antioxidant, anti-inflammatory, xanthine oxidase and tyrosinase inhibitory activities. Methanolic extracts of rapeseed meal showed significantly (P < 0.01) higher phenolics and flavonoids contents; and significantly (P < 0.01) higher DPPH and nitric oxide free radical scavenging activities when compared to that of cottonseed meal and soybean meal extracts. Ferric thiocyanate and thiobarbituric acid tests results showed rapeseed meal with the highest antioxidant activity (P < 0.01) followed by BHT, cotton seed meal and soybean meal. Rapeseed meal extract in xanthine oxidase and tyrosinase inhibitory assays showed the lowest IC(50) values followed by cottonseed and soybean meals. Anti-inflammatory assay using IFN-γ/LPS stimulated RAW 264.7 cells indicated rapeseed meal is a potent source of anti-inflammatory agent. Correlation analysis showed that phenolics and flavonoids were highly correlated to both antioxidant and anti-inflammatory activities. Rapeseed meal was found to be promising as a natural source of bioactive compounds with high antioxidant, anti-inflammatory, xanthine oxidase and tyrosinase inhibitory activities in contrast to cotton and soybean meals.     

Nanoconfined segmental dynamics in miscible polymer blend nanocomposites: the influence of the geometry of nanoparticles

The influence of nanoconfinement on segmental relaxation behavior of poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile) miscible blend and its nanocomposites with spherical and layered nanoparticles have been investigated. Dynamic mechanical analysis was employed to examine the effect of geometry of nanoparticles on the temperature dependence and relaxation function breadth of segmental dynamics (α-relaxation) in the glass transition region. The maxima of the loss modulus curves were used to fit to the Vogel–Fulcher–Tamman equation to describe the temperature dependence of the characteristic relaxation times. Furthermore, the T _g-normalized semi-logarithmic Arrhenius plots (fragility plots) were exploited to indicate the changes in cooperative segmental motions across the glass transition. The master curves for relaxation modulus were also constructed for each sample as a function of time using the time–temperature superposition principle. The investigated nanocomposites showed a narrower segmental dispersion in the glass transition region compared to the neat systems. The relaxation modulus master curves were fitted by the Kohlrausch–Williams–Watts (KWW) function. It was observed that the distribution parameter of segmental relaxation time increased with addition of nanoparticles which was correlated with a decrease in fragility index. In addition, the increase of the KWW distribution parameter (β _KWW) for spherical silica nanocomposites was less than that for nanocomposites prepared with layered silicates (organoclay).     

Using anisotropic diffusion equations in pixon domain for image de-noising

Image enhancement is an essential phase in many image processing algorithms. In any image de-noising algorithm, it is a major concern to keep the interesting structures of the image. Such interesting structures in an image often correspond to the discontinuities in the image (edges). In this paper, we propose a new algorithm for image de-noising using anisotropic diffusion equations in pixon domain. In this approach, diffusion equations are applied on the pixonal model of the image. The algorithm has been examined on a variety of standard images and the performance has been compared with algorithms known from the literature. The experimental results show that in comparison with the other existing methods, the proposed algorithm has a better performance in de-noising and preserving image edges.     

New Flower-Shaped Lead(II) Coordination Polymer at the Nano Scale: Synthesis, Structural Characterization and DFT Calculations of [Pb(o-phen)(N3)2] n Containing the Pb-(μ1,1-N3)(μ1,3-N3) Motif

A new nano-flower lead(II) azido coordination polymer from the ligand, orthophenanthroline (o-phen), [Pb(o-phen)(μ_1,1-N₃)(μ_1,3-N₃)] _n (1), was synthesized by a sonochemical method. Compound 1 was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), elemental analyses and IR spectroscopy. Single crystalline material was obtained using a heat gradient applied to a solution of the reagents. The structure of 1 is a coordination polymer of lead(II) containing the Pb-(μ_1,1-N₃)(μ_1,3-N₃) motif, formed of one-dimensional chains. The structure of the 1 was optimized by density functional theory. Structural parameters and IR spectra for 1 are consistent with the crystal structure. Pure phase PbO nanoparticles were obtained by thermolysis of 1 with oleic acid as a surfactant at 180 and 200 °C an air. PbO nanoparticles were characterized by XRD and SEM.     

Deterministic vs. stochastic performance assessment of iterative learning control for batch processes

The performance assessment of linear time‐invariant batch processes when iterative learning control (ILC) is implemented has been discussed. Previous literatures show that conventional performance assessment cannot be directly applied to batch processes due to the nature of batch operations. Chen and Kong have suggested a new method to assess the control performance of batch processes using optimal ILC as the benchmark. In their work, ILC controllers are assumed to affect either stochastic or deterministic performance but without considering their interaction. This work elaborates the controllers effects on both stochastic and deterministic control performance of batch processes. It is shown that the optimal solution based on the minimum variance control law has a trade‐off between deterministic and stochastic performance, which can be shown by a trade‐off curve. Furthermore, a method is proposed to estimate this curve from routine operating data, against which the performance of ILC controllers can be assessed. Simulation studies are conducted to verify the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 457–464, 2013     

Natural gas viscosity estimation using density based models

Accurate value determination of natural gas viscosity plays a key role in its management as it is one of the most important parameters in natural gas engineering calculations. In this study, a comprehensive model is suggested for prediction of natural gas viscosity in a wide range of pressures, temperatures, densities and compositions. The new model can be applicable for gases containing heptane plus and non‐hydrocarbon components. It is validated by the 2011 viscosity data from 18 different gas mixtures. Compared to existing similar models and correlations, its results are quite satisfactory. © 2012 Canadian Society for Chemical Engineering     

Effect of rare earth elements addition on thermal fatigue behaviors of AZ91 magnesium alloy

Influences of rare earth (RE) elements addition on thermal fatigue behaviors of AZ91 alloy were studied. Repeated heating and cooling cycles were applied on the samples at 170 and 210 ℃ to develop thermal fatigue cracks. Crack growth mechanisms and microstruc-tural influences were investigated by optical and scanning electron microscopy (SEM) as well as energy dispersive X-ray spectroscopy (EDS). Thermal fatigue behaviors were observed to improve successively by addition of the RE up to 2wt.%. This improvement was attrib-uted to the consummation of aluminum in melt by precipitation of the needle shaped A11IRE3 phases. This process was attributed to the re-duction of Mg17Al12 phase volume fraction and consequent decrease of the brittle Mg/Mg17Al12 interface which was the main reason for weak thermal properties of the alloy at rather high temperatures. Further additions of RE, however, reduced the thermal shock resistance of the samples by increasing the mean length of the brittle needle shaped phases.     

Model-based PI–fuzzy control of four-wheeled omni-directional mobile robots

The purpose of this study is to suggest and examine a PI–fuzzy path planner and associated low-level control system for a linear discrete dynamic model of omni-directional mobile robots to obtain optimal inputs for drivers. Velocity and acceleration filtering is also implemented in the path planner to satisfy planning prerequisites and prevent slippage. Regulated drivers’ rotational velocities and torques greatly affect the ability of these robots to perform trajectory planner tasks. These regulated values are examined in this research by setting up an optimal controller. Introducing optimal controllers such as linear quadratic tracking for multi-input–multi-output control systems in acceleration and deceleration is one of the essential subjects for motion control of omni-directional mobile robots. The main topics presented and discussed in this article are improvements in the presented discrete-time linear quadratic tracking approach such as the low-level controller and combined PI–fuzzy path planner with appropriate speed monitoring algorithm such as the high-level one in conditions both with and without external disturbance. The low-level tracking controller presented in this article provides an optimal solution to minimize the differences between the reference trajectory and the system output. The efficiency of this approach is also compared with that of previous PID controllers which employ kinematic modeling. Utilizing the new approach in trajectory-planning controller design results in more precise and appropriate outputs for the motion of four-wheeled omni-directional mobile robots, and the modeling and experimental results confirm this issue.