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The demand for fresh water is increasing daily, requiring industries to take action to reduce the need for fresh water. Winemaking industries represent a massive hydric impact by combining the water consumed and the high volume of wastewater produced. The sun-driven photooxidation process has been widely employed in removing wastewater pollutants. This work employed four photosensitizers, Rose Bengal, AlPcS4, ZnPcS4, and TPP, for water reuse in cellars. A secondary effluent has been investigated as a water matrix. Of all the photosensitizers (PS) employed, ZnPcS4 showed better chemical oxygen demand (COD) (23%) and phenolic (TPh) (81%) removal. The effect of pH and concentration was also assessed for ZnPcS4. The phenolic content removal was found to be highly dependent on the solution’s pH, as alkaline solutions improve the singlet oxygen quantum yield where the use of a pH = 11 reached 42% and 81% of COD and TPH removal. However, a pH higher than 7 showed higher PS bleaching than pH = 7. Three different PS concentrations were evaluated: 3 × 10−6, 5 × 10−6, and 1 × 10−5 mol/L. The optimal PS concentration was found to be 5 × 10−6 mol/L.

A refined dynamic lumped-parameter model of a REBCO field coil synchronous generator including details of the thermoelectromagnetic phenomenology of the superconductor has been developed in the classic dq0 frame of reference. The idea is to build a generic modelling framework that can be used in power systems engineering studies to infer trends on the behavior of HTS field coil machines in power grids. In the present work, the transient stability of a hybrid High Temperature Superconducting (HTS) generator is analyzed under the most severe operating condition applying a three-phase short circuit at its terminals. For the study, we chose the parameters of a wind generator at 1 MW. Two cases are considered: 1) no full transition of the HTS to the normal-resistive state under stable condition, 2) full transition of the HTS under unstable condition. The response of the HTS machine is compared against that of a conventional machine.

This article extends the semiparametric mixed model for longitudinal censored data with Gaussian errors by considering the Student's

This article presents a new and comprehensive modeling framework to carry out system-wide dynamic studies of DFIG-based wind farms embedded in multi-terminal VSC-HVDC power grids. Contrary to existing, well-developed simulation frameworks for similar studies, using electromagnetic transient (EMT) solutions, this is an RMS-type formulation which maintains a high-degree of fidelity while enabling much faster steady-state and dynamic simulations than what it is possible to achieve with EMT simulators. The new RMS modeling framework includes AC/DC power grids of an arbitrary size, topology and number of offshore VSC-connected wind farms. A simulation tool with such a high degree of modeling versatility and numerical efficiency does not currently exist elsewhere. This has required the development, using first principles, of the RMS model of a DFIG with explicit representation of all the dynamic effects relevant for dynamic problems of the electromechanical type as opposed to the study of very fast EMT phenomena. All the control functions and parameters of the rotor-side converter, the grid-side converter and the DC link, are accounted for in the new DFIG model. The prowess of the new formulation is demonstrated using a six-terminal VSC-HVDC link with two VSC-connected 200-MW wind farms. The impact of the wind farms’ operation on both the DC grid and the AC grids is assessed. The fidelity of the output results of the new simulation tool is compared against those of the EMT-type model implemented in Simscape Electrical of Simulink. The article shows that they favorably compare with each other, with differences inferior to 3%. The computational efficiency of the new dynamic modeling framework for HVDC-connected wind farms is unassailable.

Dairy industry wastewater is rich in organic content, presenting a high biodegradability, and therefore biological treatments are widely employed. This study aimed to evaluate biosolids production in three systems: activated sludge (AS), movingbed biofilm reactor (MBBR), and sequencingbatch movingbed biofilm reactor (SBMBBR). Simulated dairy wastewater was used at different organic load rates (OLRs): 1.22, 2.87, and 5.44 gCOD L−1d−1. Besides biosolids production, COD, total carbon (TC), and total nitrogen (TN) removal efficiency was evaluated. Biosolids production was measured in the mixed liquor, carrier-adhered biomass, treated wastewater, and surplus sludge. The operational conditions were kept similar for the three systems, with a carrier filling ratio of 50% for MBBR and SBMBBR. The SBMBBR proved to have better performance in the removal efficiencies of COD, TC, and TN for all OLRs studied. The MBBR presented a similar COD and TC removal efficiency as the SBBR for the two highest OLRs (2.87 and 5.44 gCOD L−1d−1). Concerning biosolids production, the MBBR system produced less biomass and delivered the lowest amount of adhered biomass inside the carriers. The AS treatment generated the highest amount of sludge and offered the worst treatment capability for all OLRs evaluated.

Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and mathematical tractability. However, this assumption seems to be restrictive when dealing with counting data. To deal with this situation, we propose a random field with a Poisson marginal distribution by considering a sequence of independent copies of a random field with an exponential marginal distribution as 'inter-arrival times' in the counting renewal processes framework. Our proposal can be viewed as a spatial generalization of the Poisson process. Unlike the classical hierarchical Poisson Log-Gaussian model, our proposal generates a (non)-stationary random field that is mean square continuous and with Poisson marginal distributions. For the proposed Poisson spatial random field, analytic expressions for the covariance function and the bivariate distribution are provided. In an extensive simulation study, we investigate the weighted pairwise likelihood as a method for estimating the Poisson random field parameters. Finally, the effectiveness of our methodology is illustrated by an analysis of reindeer pellet-group survey data, where a zero-inflated version of the proposed model is compared with zero-inflated Poisson Log-Gaussian and Poisson Gaussian copula models. Supplementary materials for this article, include technical proofs and R code for reproducing the work, are available as an online supplement.

This study assesses the methane production potential for the Anaerobic Digestion of Pig Slurry, using two different Substrate to Inoculum Ratios, 0.65, and 1, and its comparison with the theoretical prediction. The Specific Biogas Production, the Specific Methane Production, and the average methane content were determined for both experiments and adjusted to Standard Temperature (273.15 K) and Pressure (1 atm). The results reached in this study show that the experience conducted with a SIR of 1 produced more volume of biogas, with higher methane content than the one with the lower SIR. With a SIR of 1.0, it was achieved a SMP of 0.568 NL CH4/g VS with an average methane content of 83%. The Technical Digestion Time was found to be 12 days. By comparing the experimental BMP with the estimated value of 0.623 NL CH4/g VS, it was possible to conclude that the estimated value presented a deviation of just 2.96 % during the assay with a SIR of 1.

Censored data arise frequently in diverse applications in which observations to be measured may be subject to some upper and lower detection limits due to the restriction of experimental apparatus such that they are not exactly quantifiable. Mixtures of factor analyzers with censored data (MFAC) have been recently proposed for model-based density estimation and clustering of high-dimensional data in the presence of censored observations. In this paper, we consider an extended version of MFAC by considering regression equations to describe the relationship between covariates and multiply censored dependent variables. Two analytically feasible EM-type algorithms are developed for computing maximum likelihood estimates of model parameters with closed-form expressions. Moreover, we provide an information-based method to compute asymptotic standard errors of mixing proportions and regression coefficients. The utility and performance of our proposed methodology are illustrated through a simulation study and two real data examples.

A new technique to improve fault location methods in AC transmission lines is introduced in this study. The identification of fault location is carried out using one-end fault location algorithms based on impedance through dynamic phasors. This timely approach relies upon the fact that a fault resistance may introduce errors in the distance estimation when a fault takes place. Therefore, active power variations caused by the fault are used to enhance the fault location algorithms, i.e. power losses increase during the fault period for non-zero fault resistances. The present approach is extensively evaluated, using ATP-EMPT software, which relies upon the computation of a compensation factor associated with the rate of change in the resistance during pre-fault and fault conditions. This is achieved through the estimated phasors for voltage and current. Furthermore, the dynamic phasor estimation technique Taylor-Kalman-Fourier Filter is compared against a static phasor method. The proposed approach is validated using three fault location algorithms which are evaluated under different fault conditions, such as fault inception, location, and fault resistance. Results show that this proposed technique improves the fault location including fault resistances by 3% with respect to existing approaches.

In this article, the dynamic state estimation of the islanded microgrids problem is addressed. The electrical network and energy sources are represented as a set of Nonlinear Differential Algebraic...

Due to several reasons, the three-phase voltages of power grids cannot be balanced. The performance of AC–DC converters using dq0-based controls can be severely affected by the presence of unbalanced input AC voltages. Different to these proposals, this study presents a new easy-to-implement control scheme based on a single PI loop algorithm for VSC-PFC rectifiers using a phasorial approach. This new scheme has various significant advantages: (i) fast counteracting of large unbalanced voltage and current conditions; (ii) power factor = 1 at any unbalanced sag operating conditions; (iii) negligible current harmonic distortion and (iv) low-ripple DC voltage. All these features are concurrently obtained. The proposed single-PI loop VSC-PFC rectifier control strategy is theoretically and experimentally validated. A revision of the main results and characteristics of various proposed techniques that are similar to the one proposed in this study is also carried out, qualitatively indicating the main advantages featured by the proposed control strategy for VSC-PFC rectifiers.

Mixtures of factor analyzers (MFA) provide a promising tool for modeling and clustering high-dimensional data that contain an overwhelmingly large number of attributes measured on individuals arisen from a heterogeneous population. Due to the restriction of experimental apparatus, measurements can be limited to some lower and/or upper detection bounds and thus the data are possibly censored. In this paper, we extend the MFA to accommodate censored data, and the new model is called the MFA with censoring (MFAC). A computationally feasible alternating expectation conditional maximization (AECM) algorithm is developed to carry out maximum likelihood estimation of the MFAC model. Practical issues related to model-based clustering and recovery of censored data are also discussed. Simulation studies are conducted to examine the effect of censoring in classification, estimation and cluster validation. We also present an application of the proposed approach to two real data examples in which a certain number of left-censored observations are present.

This paper presents a practical approach for the real-power economic dispatch of AC/DC power transmission systems formed by VSC-HVDC equipment. It uses the Lagrange multipliers method combined with a comprehensive representation of the AC/DC power system. The power losses of the AC systems, DC grid and power converters are considered in this novel formulation by using incremental transmission loss factors. For realistic solutions, the different control strategies of the power converters forming any VSC-based power grid are considered. Interestingly, this approach permits to conclude that the AC grids coupled to converters charged with DC voltage control define the nodal marginal prices in the DC network, and in turn, the energy prices in the VSC-connected passive grids are inherited from the DC network. Overall, this modelling approach yields great flexibility to model any arbitrary VSC-based HVDC power grid interconnecting various AC systems. The developed method has been validated using a two-terminal VSC-HVDC network interconnecting two otherwise independent AC grids. Its results are compared against those obtained by the sequential quadratic programming method available in the optimisation toolbox of Matlab©, with both solutions exhibiting differences inferior to 0.30% in the total generation costs, therefore concurring very well between each other. The practicality of this approach is also demonstrated by carrying out the real-power economic dispatch of an AC/DC transmission system comprising seven VSC units which give rise to a thirteen-node DC grid.

Measurement error models constitute a wide class of models that include linear and nonlinear regression models. They are very useful to model many real-life phenomena, particularly in the medical and biological areas. The great advantage of these models is that, in some sense, they can be represented as mixed effects models, allowing us to implement well-known techniques, like the EM-algorithm for the parameter estimation. In this paper, we consider a class of multivariate measurement error models where the observed response and/or covariate are not fully observed, i.e., the observations are subject to certain threshold values below or above which the measurements are not quantifiable. Consequently, these observations are considered censored. We assume a Student-t distribution for the unobserved true values of the mismeasured covariate and the error term of the model, providing a robust alternative for parameter estimation. Our approach relies on a likelihood-based inference using an EM-type algor...

This paper introduces a novel modeling approach for short-circuit studies incorporating superconducting fault current limiters (ScFCLs). The conventional formulation for short circuit studies, based on symmetrical components, has been extended to include the nonlinear limiting features of the ScFCL. Indeed, this is a novel iterative modeling method that has been derived from the classical calculations of short circuits. The operating point of the ScFCL is computed in the abc reference frame, while the solution of the whole network, including all of its components, is achieved using symmetrical components. The ScFCL model, which includes various parallel-connected superconducting tapes, is derived for the positive, negative, and zero sequences. Given that the resistance of the ScFCL is nonlinear and depends on the current flow, an iterative process is used to carry out short-circuit calculations. To validate the proposed method, radial and meshed distribution networks were studied. The solutions derived from the proposed approach and the ones obtained from the electromagnetic transient software Alternative Transients Program-Electromagnetic Transients Program were compared with both solutions agreeing very well with each other. The standard IEEE 14-bus test system, slightly modified to include two ScFCLs, was additionally simulated to demonstrate the practicality of the new approach in larger networks.

El modelo de volatilidad estocástica en la media (SVM) propuesto por Koopman and Uspensky (2002) es re- visado. Este documento tiene dos objetivos. El primero es ofrecer una metodología que requiere menos tiempo computacional en simulaciones y estimaciones comparadas con otras propuestas en la literatura como en Abanto- Valle et al. (2021) y otros. Para lograr el primer objetivo, proponemos aproximamos la función de verosimilitud del modelo SVM aplicando Hidden Markov Models (HMM) para hacer posible la inferencia bayesiana en tiempo real. Tomamos muestras de la distribución posterior de los parámetros usando una distribución Normal mul- tivariada con media y varianza dadas por la moda posterior y la inversa de la matriz Hessiana evaluada en esta moda posterior usando importance sampling (IS). Usando simulaciones, las propiedades frecuentistas de los estimadores son analizadas. El segundo objetivo es proporcionar evidencia empírica estimando el modelo SVM utilizando datos diarios para cinco mercados bursátiles de América Latina. Los resultados indican que la volatilidad impacta negativamente en los rendimientos sugiriendo que el efecto de retroalimentación de la volatilidad es más fuerte que el efecto relacionado con la volatilidad esperada. Este resultado es exacto y opuesto al hallazgo de Koopman and Uspensky (2002). Nosotros comparamos nuestra metodología con los algoritmos de Hamiltoniano Montecarlo (HMC) y Riemannian HMC basados en Abanto-Valle et al. (2021).

The integration of intermittent renewable energy power plants, such as photovoltaic and wind power plants, to bulk power systems has changed the way their operation and expansion is performed. In this paper, an evaluation of the voltage profile of a power system considering different levels of renewable penetration and their reactive power droop control is presented. A series of power flow simulations to obtain the voltage profile and the generation's reactive power were performed using a 6-bus power system considering load-demand and renewable generation hourly curves. Based on current grid code requirements, the overall voltage control of a power system will tend to degrade with increasing penetration of renewables, as reactive power droop control alone is not sufficient to accommodate load-demand behavior due to their intermittence.

Cork boiling wastewater (CBW) is a highly polluted and difficult to treat effluent resultant from the cork manufacturing industry. This study aims to evaluate a new, reliable, efficient, and sustainable process to treat this effluent. This paper tested electrocoagulation as a pre- and post-treatment to improve the already existing physicochemical treatment in a cork production facility in Portugal. In the physicochemical procedures (PC), the addition of different volumes of coagulant (ferric chloride (III) 40% w/w), neutralizer (sodium hydroxide, 32% w/w), and flocculant (polyacrylamide, 0.2 g/L) were evaluated. Electrocoagulation (EC) was performed in a bench-scale reactor, using aluminum and stainless-steel electrodes. For EC, different initial pH, current density, and current tension values were tested. When electrocoagulation was used as a post-treatment, better performances were achieved. However, treatment costs were increased significantly. Coagulation/flocculation offers a viable and cheap treatment, achieving removal efficiencies of 88.2%, 81.0%, 76.9%, and 94.2% for total chemical oxygen demand (tCOD), total carbon (TC), total nitrogen (TN), and soluble chemical oxygen demand (sCOD), respectively. With a PC-EC combination, it is possible to achieve removal efficiencies of 92.4%, 88.0%, 91.4%, and 91.4% for tCOD, TC, TN, and sCOD, respectively. The increased TN removal efficiency can translate into great benefits for certain discharge conditions and should be taken into consideration for improving the sustainability of cork industry. On the other hand, when EC is used as a pre-treatment, there are no benefits either in terms of treatment performance or operating costs.

This paper proposes a novel modelling framework for Unit Commitment (UC) studies in multi-terminal VSC-connected AC grids. Battery Energy Storage Systems (BESS) are also considered with an energy time-shifting strategy whose charge and discharge modes are defined within the 24-hour planning horizon to promote a high competition level of energy trading in the power grid. The entire transmission network is formulated by shift factors and power losses of AC/DC branches and VSC units are included by piecewise linear functions. Thus, this novel UC approach retains a mixed-integer linear programming model with a high modelling versatility for arbitrary hybrid power grids accommodating any number of VSC stations and BESS units. The method described in this paper enables the optimal generation scheduling combined with the charge and discharge BESS modes. Its applicability is confirmed using two case studies, one featuring a four-terminal VSC-HVDC system with two BESS, and another characterised by seven VSC-connected AC systems with five BESS. To validate the proposed method, results are compared against those furnished by the classic UC representing the network through nodal equations. It is concluded that both methods favourably agree with each other as the errors are inferior to 2%. The usefulness of the proposed method to the real-time operation of AC/DC grids with BESS facilities is valuable.

In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi-parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox (1972). Theoretical investigation and simulation studies show that our method performs better than competing methods. We apply the proposed method to analyze the survival data with missing censoring indicators from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study.

Longitudinal HIV-1 RNA viral load measures are often subject to censoring due to upper and lower detection limits depending on the quantification assays. A complication arises when these continuous measures present a heavy-tailed behavior because inference can be seriously affected by the misspecification of their parametric distribution. For such data structures, we propose a robust nonlinear censored regression model based on the scale mixtures of normal distributions. By taking into account the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is considered. A stochastic approximation of the EM algorithm is developed to obtain the maximum likelihood estimates of the model parameters. The main advantage of this new procedure os to allow estimating the parameters of interest and evaluating the log-likelihood function easily and quickly. Furthermore, the standard errors of the fixed effects and predictions of unobservable values of the response can be obtained as a byproduct. The practical utility of the proposed method is exemplified using both simulated and real data.