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(1) Background: Nowadays, special attention has been paid to red and purple fruits, including blueberries and sweet cherries, since they are highly attractive to consumers due to their organoleptic properties, standing out due to their vibrant red and purple colours and sweet flavour, and nutritional value. (2) Methods: The present study evaluated the phenolic profile of phenolic-enriched extracts from blueberries and sweet cherries and explored their antioxidant potential against DPPH, superoxide and nitric oxide radicals, and ferric species, and their potential to inhibit the α-glucosidase enzyme. Furthermore, their antimicrobial activity was also determined by microdilution method against four Gram-positive strains (Enterococcus faecalis ATCC 29212, Bacillus cereus ATCC 11778, Listeria monocytogenes LMG 16779, and Staphylococcus aureus ATCC 25923) and five Gram-negative strains (Salmonella enterica subsp. enterica ATCC 13311 serovar Typhimurium, Klebsiella pneumoniae ATCC 13883, Proteus mirabilis CECT 170, Serratia marcescens CECT 159, and Acinetobacter baumannii LMG 1025). (3) Results: By chromatographic techniques, eight anthocyanins were detected in blueberry coloured fraction and total extract, and five anthocyanins were detected in sweet cherry total extract and coloured fraction, while quercetin aglycone and chlorogenic acids were the dominant non-coloured compounds in blueberries and sweet cherries, respectively. All extracts demonstrated significant antioxidant properties, as well as the ability to inhibit the activity of α-glucosidase enzyme and the development of various microorganisms. (4) Conclusion: The obtained data evidence the promising biological potential of blueberries and sweet cherries, being highly correlated with the presence of phenolic compounds.

The management of irrigation main canals are studied in this research. One way of improving this is designing an efficient automatic control system of the water that flows through the canal pools, which is usually carried out by PI controllers. However, since canal pools are systems with large time delays and nonlinear hydrodynamics, these PIs are tuned in a very conservative way so that the closed-loop instability that may appear depending on the chosen operation regime is avoided. These controllers are inefficient because they have slow time responses. In order to obtain faster responses that remain stable independently of the operation regime, a control system that combines a Smith predictor, which is appropriate to control linear systems with large time delays, with a NARX artificial neural network (ANN), that models the nonlinear dynamics of the pools, is proposed. By applying system identification procedures, two nonlinear NARX-ANN-based models and a linear mathematical model of a real canal pool were obtained. These models were applied to implement a modified NARX-ANN-based SP controller and a conventional linear SP controller. Experimental results on our real canal pool showed that our modified NARX-ANN-based SP controller overcomes conventional linear SP controllers in both setpoint tracking and load disturbance rejection.

A dead-time compensator robust H∞ state feedback controller (DTC-H∞-SFC) for the temperature control in a precalciner of a cement rotary kiln is designed. A mathematical model of the process under study with ARMAX structure was obtained. A dead-time compensator robust H∞ state feedback controller is therefore designed. The results of the comparative evaluation of the DTC-H∞-SFC vs. DTC+PI designed controllers showed that the DTC-H∞-SFC gives improved performance of the control system.

Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated.

This article addresses the design of a robust H2 state feedback controller (H2-SFC) for the effective temperature control in the heating zone of the steel slab reheating furnace. Based on the available field data and system identification procedures, a mathematical model of the heating zone is derived, which presents autoregressive-moving average with exogenous input (ARMAX) structure and fourth order. The design of an H2-SFC controller for the effective control of the heating zone temperature of the slab reheating furnace under study is developed. The simulation results of the designed control system showed its high effectiveness compared to the conventional PID control.

This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system identification procedures. Predictions of the obtained model were in good agreement with the available field data. The designed Expert MPC is distinguished by having a plant identification block and an expert system. The expert system, using a rule-based approach and the evolution of the plant variables, can modify the plant identification block, the plant prediction model, and/or the optimizer in order to improve the performance, robustness and operational safety of the overall control system. The real-time comparison results of the designed Expert MPC and a well-designed model predictive controller (MPC) show that the proposed Expert MPC has a significantly better performance and, therefore, higher accuracy and robustness.