Comparison of spectral fluorescent signatures-based models to characterize DOM in treated water samples

Statistical procedures enable a multivariate analysis of the measurements to identify specific characteristics of the dissolved organic matter (DOM) fractions in raw natural water, including the concentrations. In this work, three already established models were used to predict the concentrations of fractions of DOM from spectral fluorescent signatures (SFSs): a general linear regression (GLR), loadings and scores of a principal components analysis (PCA), and a partial least squares regression (PLS). Details about the method undertaken to prepare the fractions were given. Water samples from surface water treatment plants in New Jersey were used for the testing. In all cases, PLS have shown much better biases and accuracies than GLR and PCA models. Hydrophilic neutral, however, showed poor performances (bias 33%) due to the isolation technique used. Recommendations were provided in order to improve the DOM characterization through SFS, which linked to PLS make a powerful and cost-effective surrogate parameter to characterize DOM.     

Synthesis and enzymatic degradation of epichlorohydrin cross-linked pectins

The water solubility of pectin was successfully decreased by cross-linking with increasing amounts of epichlorohydrin in the reaction media. The initial molar ratios of epichlorohydrin/ galacturonic acid monomer in the reaction mixtures were 0, 0.37, 0.56, 0.74, 1.00, 1.47, and 2.44. The resulting epichlorohydrin cross-linked pectins were thus referred to as C-LP0, C-LP37, C-LP56, C-LP75, C-LP100, C-LP150, and C-LP250, respectively. Methoxylation degrees ranged from 60.5 ± 0.9% to 68.0 ± 0.6%, and the effective cross-linking degrees, determined by quantification of the hydroxyl anions consumed during the reaction, were 0, 17.8, 26.0, 38.3, 46.5, 53.5, and 58.7%, respectively. After incubating the different cross-linked pectins (0.5% w/v) in 25 mL of 0.05 M acetate-phosphate buffer (pH 4.5), containing 50 µL of Pectinex® Ultra SP-L (pectinolytic enzymes), between 60 and 80% of the pectin osidic bounds were broken in less than 1 hr. Moreover, increasing the cross-linking degree only resulted in a weak slowing on the enzymatic degradation velocity.     

Decision Analysis Applied to Tunnel Exploration Planning. II: Consideration of Uncertainty

Tunnel exploration is a classic problem of decision making under uncertainty. This paper, Part II, dealing with the exploration problem, discusses the sources of uncertainty in tunnel exploration, and provides techniques to formally incorporate uncertainties into the exploration planning. Part I described the state of uncertainty in geology and exploration reliability using assigned probabilities. These probabilities are, however, uncertain. Construction costs are also uncertain. This paper presents tools with which the decision maker is able to assess the effects of these uncertainties on exploration decisions. Decisions are therefore made based on the value of collecting new, uncertain information through “virtual” exploration prior to actually exploring.     

Noise Figure Reduction in Externally Modulated Analog Fiber-Optic Links

An octave-bandwidth microwave photonic link with a third-order limited spurious-free dynamic range of 121 dB in a 1-Hz bandwidth has been demonstrated. The link noise figure of 9 dB at a modulation frequency of 2 GHz was achieved by using a bias-shifted Mach-Zehnder modulator with an optical input power of 500 mW. This level of performance was realized without electronic or optical linearization     

Everything over IP — an overview of the strategic change in voice and data networks

This paper investigates the changes that are required in networking technology for ‘everything over IP’ to become a reality. Initially the changes that are taking place in the telecommunications industry are reviewed. This review ranges from a discussion about the companies installing massive global IP networks to the emergence of novel routeing technologies, e.g. multi-protocol label switching (MPLS) and terabit router technologies. The role of existing telecommunications operators is then discussed, along with the reasons why they are developing interworking and intelligence layers based on distributed computing principles to support all their networks — mobile, fixed, broadband and IP.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.     

Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

Remote sensing is widely used in coastal management. Lyzenga's model has been traditionally used to explain the relationship between bottom surface reflectance and the radiance level measured by satellite. Due to its central assumption, this model lacks accuracy compared with the other radiative transfer models. Nonetheless, it enables, with a single and simple equation, representation of the multiple optical processes taking place in coastal areas. Mapping processes associated with this model may include radiometric correction, a technique previously pointed out as a major driver of mapping accuracy. Radiometric correction is generally based on a depth-invariant index, efficient for clear waters (Jerlov water type I to II) but largely unsuitable when transparency decreases (Jerlov water type II to III). In order to overcome this problem, we developed a new index for radiometric correction, which combines bathymetry data with attenuation coefficients. The improved efficiency of our model with regard to the traditional depth invariant index was demonstrated through two case studies: Funakoshi Bay (Japan; Jerlov water type II) and the Gabes Gulf part located off Mahares (Tunisia; Jerlov water type II to III).     

A New Look at the Power Method for Fast Subspace Tracking

A class of fast subspace tracking methods such as the Oja method, the projection approximation subspace tracking (PAST) method, and the novel information criterion (NIC) method can be viewed as power-based methods. Unlike many non-power-based methods such as the Given's rotation based URV updating method and the operator restriction algorithm, the power-based methods with arbitrary initial conditions are convergent to the principal subspace of a vector sequence under a mild assumption. This paper elaborates on a natural version of the power method. The natural power method is shown to have the fastest convergence rate among the power-based methods. Three types of implementations of the natural power method are presented in detail, which require respectively O(n²p), O(np²), and O(np) flops of computation at each iteration (update), where n is the dimension of the vector sequence and p is the dimension of the principal subspace. All of the three implementations are shown to be globally convergent under a mild assumption. The O(np) implementation of the natural power method is shown to be superior to the O(np) equivalent of the Oja, PAST, and NIC methods. Like all power-based methods, the natural power method can be easily modified via subspace deflation to track the principal components and, hence, the rank of the principal subspace.