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Two-stage process of dark fermentation (DF) and photo fermentation (PF), using fruit and vegetable waste (FVW) and cheese whey powder (CWP), was used as an approach to enhance the hydrogen (H 2) production. FVW and CWP at C/N ratios of 34, 39, 60, 71 and 82 were tested as substrates for DF. Dilution (1:2, 1:5, 1:10) of the DF effluents was used as a coupling strategy. DF effluents with low-butyrate and high lactate concentrations were obtained as a function of an increased C/N ratio, which results in high H 2 production during the PF. Maximum overall H 2 yields of 793.7 and 695.4 mLH 2 /gChemical Oxygen Demand (COD) were obtained using a 1:10 dilution, at a C/N ratio of 60 and 70, respectively. These H 2 yields were higher than those obtained with the individual processes. The C/N ratio at the DF stage regulate not only H 2 production but also the distribution and concentrations of by-products. These metabolites, in turn, control the H 2 production during the PF. Predominant microbial population for both processes (DF: C/N = 34 Acetobacter lovaniensis , Clostridium butyricum ; C/N = 39 C. butyricum, Enterobacter sp , Bifidobacterium ; C/N = 82 Lactobacillus casei ; PF: Rhodopseudomonas palustris) were in accordance with the final metabolic products. Image 1 • Enhanced H 2 production by dark-photo (DF-PF) fermentations was demonstrated. • Fruit and vegetable waste and cheese whey as substrate allows waste removal and H 2 production. • Increased C/N ratio reduces H 2 yield at DF, but increase the H 2 production at PF. • DF effluents with high lactate concentrations promotes high H 2 production at PF. • Microbial population for both DF, PF in accordance with the final metabolic products. [ABSTRACT FROM AUTHOR]

The aim of this study is to design and implement a virtual reality bicycle system based on a functional-based mechatronic design approach. The development of virtual reality technologies with haptic systems demands a proper integration of the involved disciplines to provide immerse experiences for users. The proposed design approach provides a formal manner to gather the subsystems in the mechatronic device. The developed system is divided in a Virtual Reality System (VRS) and a Physical System (PS) for the design process. The former includes an interactive virtual environment in which an Avatar is animated using a simple kinematic bicycle model. The latter includes an adapted mountain bicycle with haptic feedback mechanisms to interact with the user and to produce the corresponding inputs for the bicycle model. Both systems are integrated by a control behavior system that works under two operation modes, where the user carries out virtual tours and gets feedbacks from a stereoscopic display system, audio cues, and haptic mechanisms. A multibody simulation validates the consistency and the integration of the physical system. In addition, a set of experimental results show the performance of instrumentation elements, control strategies, and feedback mechanisms, to provide the user with an immersive experience in the virtual environment. A brief survey was carried out to assess the opinion of users about the virtual bicycle tours, providing feedback for future improvements. The different designed modules and sub-systems allow modifying and enhancing the VRS without major modifications of the PS, or allow enhancing the physical platform without affecting the functionality of the virtual environment. • Integration of several technologies for a virtual reality system using a VDI 2206 guideline. • Modular configuration for enhancement of the system. • Low latency times obtained, allowing a fluid an immersive experience. • Control-behavior subsystem to configure the operating mode of the system. • Proportional and ADRC strategies for haptic feedback. [ABSTRACT FROM AUTHOR]

Abstract In this paper, an adaptive disturbance estimation-based control of a class of uncertain feedback linearizable systems with the presence of, both, external perturbations as well as non-modeled dynamics is considered. The aim of the control design was to solve the tracking trajectory problem for a class of output-based linearizable uncertain systems. An adaptive scheme is proposed for developing a state estimator of the uncertain dynamics. The estimation of both, the states and the uncertain dynamics is attained despite the limited knowledge of the plant and the information contained in the output signal. The uncertain section in the linearized system was approximated by a class of time-dependent combination of the system states. The observer implemented a parametric identifier to obtain the time varying parameters associated to the estimation of the uncertain section. This method ensured the adequate estimation process of the uncertainties/perturbations, measured in terms of the mean square error. Simultaneously, an adaptive gain associated to the observer adjusts its trajectories to provide the ultimate boundedness of the estimation error. Once the states of the uncertain system are obtained, a feedback controller rejects actively the perturbations that affect the system by a compensation scheme. Two numerical examples were developed to show the observer-based control performance. Highlights • The controller is based on an adaptive observer in order to estimate uncertainties. • The adaptive estimator uses a classic system identification procedure (LMS). • The stability analysis was based on an innovative Lyapunov theory. • A state observer and a time varying identifier formed the adaptive approach. • The Lyapunov analysis leads to find that origin is the practical stable equilibrium point. [ABSTRACT FROM AUTHOR]

The effect of the flux patterns promoted by a reactor's impeller distribution on the biological hydrogen (bioH 2 ) production by a microbial consortium was determined. The flux patterns were analyzed and characterized by the application of computational fluid dynamics (CFD, ANSYSS Fluent 14.5). Two different mixing systems; predominantly axial (pitched blade PB4) or radial flow (Rushton) impellers were evaluated. Based on CFD results, four different impeller configurations were experimentally assessed to produce bioH 2 . The highest bioH 2 productivity of 440 mL/Lh was determined with PB4 impellers, under the best configuration. In the second-best configuration, also obtained with the PB4, a bioH 2 productivity of 407.94 mL/Lh was measured. The configurations based on Rushton impellers showed lower bioH 2 productivity (177.065 mL/Lh average). Therefore, the experiments where the axial pumping was favored showed the highest bioH 2 production as a consequence of the enhanced transfer of the bioH 2 from the liquid phase to the reactor headspace. [ABSTRACT FROM AUTHOR]

Most of the research in sliding mode theory has been carried out to in continuous time to solve the estimation and control problems. However, in discrete time, the results in high order sliding modes have been less developed. In this paper, a discrete time super-twisting-like algorithm (DSTA) was proposed to solve the problems of control and state estimation. The stability proof was developed in terms of the discrete time Lyapunov approach and the linear matrix inequalities theory. The system trajectories were ultimately bounded inside a small region dependent on the sampling period. Simulation results tested the DSTA. The DSTA was applied as a controller for a Furuta pendulum and for a DC motor supplied by a DSTA signal differentiator. [ABSTRACT FROM AUTHOR]

This paper deals with a switching robust tracking feedback design for a corona-effect ozone generator. The generator is considered as a switched systems in the presence of bounded model uncertainties as well as external perturbations. Three nonlinear dynamic models under arbitrary switching mechanisms are considered assuming that a sample-switching times are known. The stabilization issue is achieved in the sense of a practical stability. We apply the newly elaborated (extended) version of the conventional attractive ellipsoid method (AEM) for this purpose. The same analysis was efficient to obtain the minimal size of region where the tracking error between the trajectories of the ozone generator and reference states converges. The numerically implementable sufficient conditions for the practical stability of systems are derived based on bilinear matrix inequalities (BMIs). [ABSTRACT FROM AUTHOR]

Abstract: Biofiltration is an economical and environmentally friendly process to eliminate air pollutants. Results obtained by different authors showed the enhanced performance of the fungal biofiltering systems. Consequently, there is a necessity to develop methodologies not only to design more efficient reactors but to control the reaction behavior under different conditions: pollutants feeding, air flows, humidity and biomass production. In this study, a continuous neural network observer was designed to predict the toluene vapors elimination capacity (EC) in a fungal biofilter. The observer uses the carbon dioxide (CO2) production and the pressure drop (DP) (on line measurements) as input information. The differential neural network observer proved to be a useful tool to reconstruct the immeasurable on-line variable (EC). The observer was successfully tested under different reaction conditions proving the robustness of estimation process. This software sensor may be helpful to derive adaptive control functions optimizing the biofilter reaction development. [Copyright &y& Elsevier]

Abstract: Presented in this study, a dynamic neural network (DNN) is employed to estimate the states dynamics of the phenols-ozone-water system. A new technique based on the dynamic neural network observer (DNNO) with relay (signum) term is applied to estimate the decomposition dynamics of phenols and to identify their kinetic parameters without any mathematical model usage. The decomposition of phenols (phenol (PH), 4-chlorophenol (4-CPH) and 2,4-dichlorophenol (2,4-DCPH)) and their mixture by ozone, realized in a semi-batch reactor, is considered as a process with uncertain model (“black-box”). Only one parameter monitoring, namely, the ozone concentration in gas phase in the reactor outlet, is measured during ozonation. The variation of this variable is used to obtain the summary characteristic curve for the phenols ozonation. Then, using the experimental decomposition dynamics of phenols and of their mixture, obtained by HPLC method, the proposed DNNO is applied to estimate the ozonation constants of phenols at the different pH 2–12. A good correspondence between the decomposition dynamics and the estimated ones by DNNO is obtained. [Copyright &y& Elsevier]

• The paper presents four recurrent and differential artificial neural networks (ANN) structures to construct different versions of dynamic automatic pattern classifiers. • Two different annotated and validated databases of diverse physiological signals were used to evaluate the capacities of all the ANNs proposed in this study. • Two validation methods were used to justify the application of dynamic ANNs as pattern classifiers: generalization-regularization and k-fold cross validation. • The recurrent neural network was also implemented in a 32-bits microcontroller embedded device. In the last few years, recurrent and continuous algorithms have became key factors in the solution of diverse pattern recognition problems. The main goal of this study is to introduce four classes of recurrent and continuous artificial neural networks (ANN) that can be implemented for pattern recognition of electrophysiological signals. Such networks are generally known as dynamic neural networks (DNN). The proposed DNN based pattern recognizer uses biosignals raw data as input. This processing method allows capturing the signal time dynamics, which is considered as an intrinsic characteristic of physiology signals. Therefore, recurrent and differential ANN structures were developed to construct different versions of dynamic automatic pattern recognizer. The first one describes the application of Recurrent Neural Networks (RNN) to enforce the biosignal analysis which evolves over time with a fixed sampling period. Three different DNNs with continuous dynamics are introduced. Differential neural network (DifNN) with the capability of learning the evolution of the signal in continuous time, a time-delay neural network (TDNN) for classification is implemented to consider the time-delayed characteristics of the electrophysiological signals and a complex valued neural network (CVNN) which considered the signals to be classified may be pre-processed with a frequency analysis technique. Two different databases of diverse physiological signals are used in this study to validate the application of dynamic neural networks. A first database considers electromiographic (EMG) signals which are tested using the DifNN, TDNN and CVNN. The second database includes gait in Parkinson's disease database signals which are used in the evaluation procedure of RNN. Two validation methods are used to justify the application of dynamic ANNs as pattern recognizer for the EMG activities and the health level classification of patients suffering from Parkinson's: generalization-regularization and the k -fold cross validation. The accuracy estimation and the confusion matrix evaluation confirm the superiority of the proposed approach compared to classical feed-forward ANN pattern recognizer. The particular case of the RNN is also implemented in a 32-bits micro-controller embedded device. [ABSTRACT FROM AUTHOR]

This paper presents the development of a hybrid model that describes the temperature, elongation and inner force relationships in a spring Shape Memory Alloy (SMA) actuator. The temperature-inner force relationship obeys a hybrid structure in which a sigmoid function correlates the variation of temperature for the SMA and the force executed by the spring actuator. The hybrid nature of the model describes the regular hysteresis behavior of the SMA. The switching law of the hybrid model depends on the time derivative of the temperature. A multivariable model depending on temperature, and inner and external forces was developed in order to characterize the Shape Memory Effect (SME). A set of experiments was carried out to obtain the parameters used to characterize the model. The application of the Levenberg-Marquardt method resulted in the parametric estimation procedure. An averaged correlation factor of 0.95 between the model response and experimental results justifies the proposed modeling approach. [ABSTRACT FROM AUTHOR]

This study reports the design and implementation of a pattern recognition algorithm aimed to classify electroencephalographic (EEG) signals based on a class of dynamic neural networks (NN) described by time delay differential equations (TDNN). This kind of NN introduces the signal windowing process used in different pattern classification methods. The development of the classifier included a new set of learning laws that considered the impact of delayed information on the classifier structure. Both, the training and the validation processes were completely designed and evaluated in this study. The training method for this kind of NN was obtained by applying the Lyapunov theory stability analysis. The accuracy of training process was characterized in terms of the number of delays. A parallel structure (similar to an associative memory) with fixed (obtained after training) weights was used to execute the validation stage. Two methods were considered to validate the pattern classification method: a generalization-regularization and the k -fold cross validation processes ( k = 5). Two different classes were considered: normal EEG and patients with previous confirmed neurological diagnosis. The first one contains the EEG signals from 100 healthy patients while the second contains information of epileptic seizures from the same number of patients. The pattern classification algorithm achieved a correct classification percentage of 92.12% using the information of the entire database. In comparison with similar pattern classification methods that considered the same database, the proposed CNN proved to achieve the same or even better correct classification results without pre-treating the EEG raw signal. This new type of classifier working in continuous time but using the delayed information of the input seems to be a reliable option to develop an accurate classification of windowed EEG signals. [ABSTRACT FROM AUTHOR]

This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization–regularization and a k -fold cross validation ( k = 5 ). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results. [ABSTRACT FROM AUTHOR]

Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov–Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN. [ABSTRACT FROM AUTHOR]

The co-digestion process of crude cheese whey (CCW) with fruit vegetable waste (FVW) for biohydrogen production was investigated in this study. Five different C/N ratios (7, 17, 21, 31, and 46) were tested in 2 L batch systems at a pH of 5.5 and 37 °C. The highest specific biohydrogen production rate of 10.68 mmol H2/Lh and biohydrogen yield of 449.84 mL H2/g COD were determined at a C/N ratio of 21. A pyrosequencing analysis showed that the main microbial population at the initial stage of the co-digestion consisted of Bifidobacterium, with 85.4% of predominance. Hydrogen producing bacteria such as Klebsiella (9.1%), Lactobacillus (0.97%), Citrobacter (0.21%), Enterobacter (0.27%), and Clostridium (0.18%) were less abundant at this culture period. The microbial population structure was correlated with the lactate, acetate, and butyrate profiles obtained. Results demonstrated that the co-digestion of CCW with FVW improves biohydrogen production due to a better nutrient balance and improvement of the system's buffering capacity. [ABSTRACT FROM AUTHOR]

This article deals with the control of a class of robotic systems with constant input time delay through the Active Disturbance Rejection paradigm, by means of Generalized Proportional Integral observers. The system is represented as a linear perturbed system, whose lumped disturbance input is estimated and then compensated by an Extended State Observer-Based Control, which is implemented on a predictor scheme for the time delay compensation. A complete Lyapunov Krasovskii functional was used to perform the stability analysis, leading to an ultimate bound trajectory tracking error. The proposal is tested and validated on a two degrees of freedom robotic manipulator. [ABSTRACT FROM AUTHOR]

Three microbial consortia (C2, C4, C5) grown under photoheterotrophic conditions assimilated acetate and butyrate, as individual and mixed substrates. By controlling the culture conditions, it was possible to manipulate the microbial population composition and thus the yield of polyhydroxyalkanoates (PHA) accumulation. Under limited ammonium conditions, pH control, and a sequential two-step process, C2 and C4 produced PHA. C4 showed the highest production of 44% of the cell dry mass (CDM), close to the theoretical value calculated with a stoichiometric balance. Analysis of the confocal microscopy images confirmed the accumulated biopolymer percentages produced by each consortium, and it was in close correlation with microbial distribution and substrate consumption pattern. 1 H, 13 C, NMR, and MALDI-TOF spectra identified the primary structure of the obtained biopolymers as copolymers of 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV). This composition allows for better mechanical properties compared to the PHB homopolymer. Microbial characterization showed a similar microbial population with different proportions for C2 and C4. The highest PHA production in C4 was associated with higher abundances of PHA producers, including Clostridium (29%), Pseudomonas (8%) and Rhodopseudomonas (5%). Both microbial consortia showed that a portion of their microbial populations were able to perform syntrophic reactions ( Dysgonomonas and Clostridium ). [ABSTRACT FROM AUTHOR]

In the study presented in this paper , the problem of the design and implementation of a two-axis sun tracking system was addressed by applying a set of two robotic systems, one for the automatic orientation of the photovoltaic modules and the second for providing the reference trajectory (robotic sensor). The design methodology was based on mechatronic concepts, in particular the VDI 2206 standard, according to which the system is divided into interconnected modules to be designed, validated, and integrated. This approach provides an efficient energy collection system in terms of the mechanism, instrumentation system, energy supply, and automatic trajectory tracking control. Experimental results illustrate the behavior of the proposed system, which achieves a better performance than fixed systems, as well as one-axis tracking mechanisms. [ABSTRACT FROM AUTHOR]

The aim of this study was to characterize the ozone reaction with phenanthrene adsorbed in two types of soils (sand and agricultural). The effect of soil physicochemical properties (texture, bulk density, particle density, porosity, elemental composition, permeability, surface area and pore volume) on the phenanthrene decomposition was evaluated. Commercial sand has a uniform morphology (spherical) with a particle size range between 0.178 and 0.150 mm in diameter, regular elemental composition SiO 2 , specific density of 1701.38 kg/m 3 , a true density of 2492.50 kg/m 3 , with an effective porosity of 31%. On the other hand, the agricultural soil had heterogeneous morphology, particle size between 0.1779 and 0.05 mm in diameter, elemental composition was montmorrillonite silicon oxide, apparent density of 999.52 kg/m 3 , a true density of 2673.55 kg/m 3 , surface area of 34.92 m 2 /g and porosity of 57%. The percentage of phenanthrene decomposition in the sand was 79% after 2 h of treatment. On the other hand, the phenanthrene degradation in the agricultural soil was 95% during the same reaction time. The pore volume of soil limited the crystal size of phenanthrene and increased the contact surface with ozone confirming the direct impact of physicochemical properties of soils on the decomposition kinetics of phenanthrene. In the case of agricultural soil, the effect of organic matter on phenanthrene decomposition efficiency was also investigated. A faster decomposition of initial contaminant and byproducts formed in ozonation was obtained in natural agricultural soil compared to the sand. The partial identification of intermediates and final accumulated products produced by phenanthrene decomposition in ozonation was developed. Among others, phenanthroquinone, hydroquinone, phenanthrol, catechol as well as phthalic, diphenic, maleic and oxalic acids were identified. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of an output feedback controller based on the super twisting algorithm (STA) that stabilizes the trajectory tracking error of a skid steered mobile robot (SSMR). The control scheme introduces a diffeomorphism based on the mathematical model of the SSMR to transform the original problem into a third order chain of integrators. In this study, the available measurements are the position and orientation of the SSMR. A modified STA working as a step by step differentiator estimates the velocity and acceleration of the mobile robot. Then, a second STA enforces the tracking of a predefined trajectory. Numerical and experimental results comparing the STA with a state feedback controller (SFC) and a first order sliding mode controller (FOSM) justify the control proposal. [ABSTRACT FROM AUTHOR]

Sequential processes appear naturally in all types of industries. Biotechnology is a good example of such schemes. Wastewater treatment using microbiological activity is a particular case having all the characteristics of sequential methods. Sulfate reduction as pre-treatment followed by the decomposition of sulfated compounds using adapted microorganisms is the sequential nonlinear process with state constrains analyzed in this paper. Modeling this procedure is still a difficult task because the number of elements involved in the reaction. This paper presents an adaptive algorithm to obtain a suitable model of this process using continuous neural networks. The adaptive model preserves the sequential nature of the process as well as the bounded nature of all states. The neural network is proposed as a system identifier in terms of the hybrid systems theory. The Lyapunov stability method is used to demonstrate the convergence of the identifier states to the real concentrations of the microbiological system. Experimental results and their corresponding simulation using the adaptive model based on neural networks confirm the theoretical results described in this paper. [ABSTRACT FROM AUTHOR]

The fuzzy logic controller (FLC) has the ability of handling parametric uncertainties and external perturbations for unknown systems. A regular structure for a FLC is the proportional derivative (PD) form. The proportional derivative fuzzy controller (PDF) could be seen as a variable gain PD controller. Despite this characteristic, the most common drawback for any PD controller, with unknown dynamics or even with unmodeled dynamics is the error signal differentiation. In this manuscript this disadvantage was overtaken implementing the super-twisting algorithm (STA) as a robust exact differentiator (RED). The information provided by the STA was injected into the PDF to enhace its performance. In this study, the stability of the nonlinear system under the fuzzy super twisting PD controller (FSTPD) in closed loop was analyzed using the concept of the second Lyapunov׳s method. Numerical simulations were designed to show the effectiveness and advantages of the proposed FSTPD over the classical PD structure supplied with the STA and a PDF with the derivative part obtained by a linear filter. A first example to stabilize a simple pendulum was developed applying the FSTPD. A second example for solving a tracking control problem was designed for a robot manipulator with six degrees of freedom. In both cases, the FSTPD showed better performance and a significant reduction of the control energy. [ABSTRACT FROM AUTHOR]

Three FTO-BiVO 4 photoelectrodes are fabricated modifying the BiVO 4 thickness, and systematically evaluating the influence of FTO substrate on the optical, electrical properties, and photoelectrochemical performance of BiVO 4 semiconductor. The catalysts are characterized using two light sources with back-side or front-side irradiations, to investigate the impacts of different energy sources and configuration illumination on the FTO-BiVO 4 photoactivity. This analysis reveals the existence of an additional charge transfer resistance increasing with thickness film subjected to front-side illumination, while the resistance remarkably diminishes when this interface is directly irradiated under back-side illumination. The highest photocurrent is achieved with the LED lamp under back-side illumination, condition selected to compare the degradation of 20 mg L−1 ciprofloxacin (CIP) in 0.05 M NaCl through electrocatalysis, photocatalysis, and photoelectrocatalysis using front-side or back-side illuminations. In these evaluations, modified FTO contributes to the photogeneration of reactive chlorine species, whence it cannot be considered as a simple substrate. Back-side illumination presents a higher CIP elimination in comparison with front-irradiation. A schematic energy band diagram relying on Tauc and Mott-Schottky plots, and incorporating FTO as a photoactive semiconductor is established to rationalize the formation of oxidant species in the system. A degradation mechanism is established based on HPLC measurements of the different treatment methods. [Display omitted] • Light source affects the formation of a true FTO-BiVO 4 heterojunction. • FTO beyond a current collector influencing the photoelectrochemical performance. • Charge transfer resistance increasing with FTO-BiVO 4 thickness at front radiation. • FTO contributes to photogenerate reactive chlorine species (non-substrate). • Back-side illumination presents higher Ciprofloxacin elimination than front one. [ABSTRACT FROM AUTHOR]