Gilles G. Patry

Grey integer programming: An application to waste management planning under uncertainty

Abstract This paper introduces a grey integer programming (GIP) method for facility expansion planning under uncertainty, by incorporating the concepts of grey number and grey mathematical programming into a mixed integer linear programming optimization framework. The approach is an improvement upon previous integer programming methods in terms of its technical characteristics and applicability. It allows uncertain information to be effectively communicated into the optimization process and the resulting solutions. It also has low computational requirements and is thus applicable to practical problems. The modelling approach is applied to a hypothetical planning problem of waste flow allocation and treatment/disposal facility expansion within a regional solid waste management system. The binary variable solutions provide the ranges of different development alternatives within a multi-period, multi-facility and multi-scale context, and the continuous variable solutions provide optimal schemes for waste flow allocation corresponding to the upper and lower bounds of the objective function value. The results indicate that reasonable and useful solutions can be achieved through the developed approach.

Grey fuzzy integer programming: An application to regional waste management planning under uncertainty

Abstract This paper introduces a grey fuzzy integer programming (GFIP) method and its application to regional solid waste management planning under uncertainty. The GFIP improves upon the existing integer programming methods by incorporating both grey fuzzy linear programming (GFLP) and grey integer programming (GIP) approaches within a general optimization framework. The approach allows uncertainty in both model coefficients and stipulations to be effectively communicated into the optimization process and resulting solutions, such that feasible decision alternatives can be generated through appropriate interpretation of the solutions. Moreover, the GFIP does not lead to more complicated intermediate models in its solution process, thus offering lower computational requirements than existing methods. In addition, it is applicable to practical problems. The modelling approach is applied to a hypothetical planning problem of waste management facility expansion/utilization planning within a regional solid waste (RSW) management system. The results indicate that reasonable solutions were generated for both binary and continuous variables. The binary variable solutions represent the related grey decisions of waste management facility expansion within a multi-period, multi-facility and multi-scale context. Further, they have been interpreted to provide decision alternatives that reflect the effects of uncertainties. The continuous variable solutions relate to grey decisions for waste flow allocation corresponding to the suggested facility expansions.

GREY QUADRATIC PROGRAMMING AND ITS APPLICATION TO MUNICIPAL SOLID WASTE MANAGEMENT PLANNING UNDER UNCERTAINTY

Abstract This paper introduces a grey quadratic programming (GQP) method asa means for decision making under uncertainty. The method improves upon existing grey linear programming (GLP) methods by allowing the consideration of the effects of economies of scale on cost coefficients in the objective function. The approach also has advantages over a grey nonlinear programming method, since a global optimum is obtainable and the model is moderately easy to solve through commercially available quadratic programming packages. The modelling approach is applied to a hypothetical problem of waste flow allocation within a municipal solid waste management system. The results indicate that, compared with the GLP method, GQP provides a more effective means for reflecting system cost variations and may therefore generate more realistic and applicable solutions.

Intelligent robotic sensor agents for environment monitoring

This paper discusses development aspects of a wireless network of mobile autonomous Robotic Intelligent Sensor Agents deployed in the field for active investigation of multiple environmental parameters. The collected sensor data are fused in a world model, which is available to remote human supervisors as an interactive virtual Virtualized Reality/spl trade/ environment model.

WASTE FLOW ALLOCATION PLANNING THROUGH A GREY FUZZY QUADRATIC PROGRAMMING APPROACH

Abstract This paper proposes a grey fuzzy quadratic programming (GFQP) approach as a means for optimization analysis under uncertainty. The method combines the ideas of grey fuzzy linear programming (GFLP) and fuzzy quadratic programming (FQP) within a general optimization famework. It improves upon the previous GFLP method by using n grey control variables, ® (A,j (i = 1,2,..., n), for n constraints instead of one ® (X) for n constraints in order to incorporate the independent properties of the stipulation uncertainties; it also improves upon the FQP method by further introducing grey numbers for coefficients in A and C to effectively reflect the lefthand side uncertainties. Compared with the GFLP method, the GFQP approach is helpful for better satisfying model objective/constraints and providing grey solutions with higher system certainty and lower system cost; compared with the FQP method, more information of the independent uncertain features of not only the stipulations but also the lefthand side coe...

Regional flood frequency analysis using GIS, L-moment and geostatistical methods

Advances in space -time tools and techniques offer new possibilities to improve methods for exploratory data analysis and parameter estimation in regional flood frequency analysis (FFA). A general framework and methodological approach are proposed which integrate concepts and techniques of regional FFA, geostatistical theory and analytical geographical information systems (GIS) using data on climate, vegetation, geography and flood timing and magnitude statistics. Non-parametric methods are used to screen data and to delineate homogeneous regions. Simulations are used to identify discordant sites, diagnose each region using the signal-to-noise ratio and test regions for homogeneity based on L-moment ratios. Geostatistical measures of spatial autocorrelation are used to diagnose hierarchical spatial models for each L-moment ratio and to obtain map estimates of parameters using spatially explicit kriging techniques (analogous to regression). In addition to storing and displaying the spatio-temporal information accurately, the GIS is used to quantify spatial associations between dependent and independent variables and to diagnose homogeneous regions for further refinement using a simple spatial contrast measure. Analysis of data from central and eastern Canada (except the eastern parts of Newfoundland), encompassing a large area with significant random and systematic variability, demonstrates that: (i) map sets of L-mean and L-CV (coefficient of variation) for flood timing and magnitude can serve as indicators of climatic influences on the flood-generating mechanisms; (ii) models of spatial autocorrelation can be used to map point variables and their geostatistical spatial variance, which indicates whether maps are significant; and (iii) associations between L-CV and snow or vegetation could support improved mapping using co-kriging or geostatistical simulations. The mapbased method provides parameter values at ungauged sites and maps of spatial variance that could support decisions to add or remove gauges from a hydrometric network.

Annual maxima and partial duration flood series analysis by parametric and non-parametric methods

Annual maxima (AM) and partial duration (PD) flood series are modelled by parametric and non-parametric methods. In PD analysis the number of threshold exceedances is assumed to be Poisson distributed: the peak exceedances are described by the generalized Pareto (GP) and non-parametric (NP) distributions. The generalized extreme value (GEV) and non-parametric (NP) distributions are used to describe the AM series. L-moments are employed for parameter estimation for GEV and GP distributions. Analysis of data from the provinces of Quebec and Ontario, Canada, shows that both AM and PD series can be inferred as being unimodal and bimodal, both of which can be described by the NP method. Also, this method is found not to be sensitive to the choice of threshold level; however, it was also observed that parametric methods cannot detect biomodality, give different quantile estimates for AM and PD data and PD estimates are sensitive to the selection of threshold level. Therefore, the NP method is more advantageous than the parametric methods in flood frequency analysis for both AM and PD series.

Neural-network-based models of 3-D objects for virtualized reality: a comparative study

The paper presents a comprehensive analysis and comparison of the representational capabilities of three neural architectures for three-dimensional (3-D) object representation in terms of purpose, computational cost, complexity, conformance and convenience, ease of manipulation, and potential applications in the context of virtualized reality. Starting from a pointcloud that embeds the shape of the object to be modeled, a volumetric representation is obtained using a multilayer feedforward neural network (MLFFNN) or a surface representation using either the self-organizing map (SOM) or the neural gas network. The representation provided by the neural networks (NNs) is simple, compact, and accurate. The models can be easily transformed in size, position, and shape. Some potential applications of the presented architectures in the context of virtualized reality are for the modeling of set operations and object morphing, for the detection of object collision, and for object recognition, object motion estimation, and segmentation.

IDENTIFICATION AND QUANTIFICATION OF NITROGEN NUTRIENT DEFICIENCY IN THE ACTIVATED SLUDGE PROCESS USING RESPIROMETRY

Experimental protocols to identify and quantify nitrogen nutrient deficiency in the activated sludge process were developed and tested using respirometry. Respirometric experiments showed that when a nitrogen nutrient deficient sludge is exposed to ammonia nitrogen, the oxygen uptake rate (OUR) of the sludge increases while an initial nitrogen nutrient uptake takes place at the same time. Our investigation suggests that this initial nitrogen uptake is metabolically mediated. The protocols developed in this study can be used: (a) to assess whether a sludge sample is nitrogen nutrient deficient or not; and (b) to estimate the amount of nitrogen required to remedy a nutrient deficient condition in the sludge. Finally, a respirometry-based strategy to control nutrient addition to the activated sludge process is proposed.

Constructing a model hierarchy with background knowledge for structural risk minimization: application to biological treatment of wastewater

This article introduces a novel approach to the issue of learning from empirical data coming from complex systems that are continuous, dynamic, highly nonlinear, and stochastic. The main feature of this approach is that it attempts to integrate the powerful statistical learning theoretic methods and the valuable background knowledge that one possesses about the system under study. The learning machines that have been used, up to now, for the implementation of Vapniks inductive principle of structural risk minimization (IPSRM) are of the black-box type, such as artificial neural networks, ARMA models, or polynomial functions. These are generic models that contain absolutely no knowledge about the problem at hand. They are used to approximate the behavior of any system and are prodigal in their requirements of training data. In addition, the conditions that underlie the theory of statistical learning would not hold true when these black-box models are used to describe highly complex systems. In this paper, it is argued that the use of a learning machine whose structure is developed on the basis of the physical mechanisms of the system under study is more advantageous. Such a machine will indeed be specific to the problem at hand and will require many less data points for training than their black-box counterparts. Furthermore, because this machine contains background knowledge about the system, it will provide better approximations of the various dynamic modes of this system and will, therefore, satisfy some of the prerequisites that are needed for meeting the conditions of statistical learning theory (SLT). This paper shows how to develop such a mechanistically based learning machine (i.e., a machine that contains background knowledge) for the case of biological wastewater treatment systems. Fuzzy logic concepts, combined with the results of the research in the area of wastewater engineering, will be utilized to construct such a machine. This machine has a hierarchical property and can, therefore, be used to implement the IPSRM.