Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System

One of the biggest challenges in water quality monitoring is how to optimize big Data gathered from a wide range of resources. This paper presented a new software-based pathway of process mining approach for extending a flexible WQI (Water Quality Index) that would deal with uncertainties derived from missing data occurrence in short- and long-term assessments. The methodology is based on integration of four multi-criteria group decision-making models coupled with fuzzy simulation including AHP (Analytical Hierarchy Process), fuzzy OWA (Ordered Weighting Average), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and fuzzy TOPSIS that were used for data mining and group consensus evaluation.. Examining the methodology on groundwater resources being supplied for drinking in Shiraz, Iran showed high integrity, accuracy, and proximity-to-real interpretation of water quality. This was the first study where decision-making risks such as Decision Makers’ risk-prone or risk-aversion attitudes (optimistic degree), DMs’ power, and consensus degree of each water quality parameter have been considered in WQI research. The proposed index offered a flexible choice in defining the intended project duration, stakeholders’ judgments, types of water use and water resource, standards, as well as type and number of water quality parameters. Thus, beside sustaining the unity in structure, this methodology could be suggested as a potentially WQI for other regions. The presented methodology would help more efficient monitoring of water resources for drinking purpose with respect to water quality.

     

Nonlinear Interval Parameter Programming Combined with Cooperative Games: a Tool for Addressing Uncertainty in Water Allocation Using Water Diplomacy Framework

Water Resources Management - This paper shows the utility of a new interval cooperative game theory as an effective water diplomacy tool to resolve competing and conflicting needs of water users...     

Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling

Urmia Lake in Iran is the second largest saline lake in the world. This ecosystem is the home for different species. Due to various socio-economical and ecological criteria, Urmia Lake has important role in the Northwestern part of the country but it has faced many problems in recent years. Because of droughts, overuse of surface water resources and dam constructions, water level has decreased in such a way that one quarter of the lake has changed to saline area in the last 10 years. The purpose of this research is to determine the main factors which reduce the lake’s water level. To this end, a simulation model, based on system dynamics method, is developed for the Urmia Lake basin to estimate the lake’s level. After successful verification of the model, results show that (among the proposed factors) changes in inflows due to the climate change and overuse of surface water resources is the main factor for 65% of the effect, constructing four dams is responsible for 25% of the problem, and less precipitation on lake has 10% effect on decreasing the lake’s level in the recent years. In the future, the model also can be used by managers as a decision support system to find the effects of building new dams or other infrastructures.     

Investigation of Hydrodynamics of High‐Temperature Fluidized Beds by Pressure Fluctuations

Hydrodynamics of a gas‐solid fluidized bed at elevated temperatures was investigated by analyzing pressure fluctuations in time and frequency domains. Sand particles were fluidized with air at various bed temperatures. At a constant gas velocity, the standard deviation, power spectrum density function, and wide‐band energy of pressure fluctuations reach a maximum at 300 °C. Increasing the temperature to this value causes larger bubble sizes and after the bubbles reach their maximum size, they break into smaller bubbles. The Archimedes number decreases with higher temperature and the type of fluidization becomes closer to that of Geldart A boundary at this maximum temperature. Based on estimation of the drag force acting on the emulsion phase, it was concluded that 300 °C was a transition temperature at which the drag force reaches a minimum due to a significant change of interparticle and hydrodynamic forces.     

Non‐intrusive characterization of particle size changes in fluidized beds using recurrence plots

An on‐line method is developed for monitoring of mean particle size in fluidized beds using pressure fluctuations (PFs) and acoustic emissions (AE) signal by recurrence plot (RP) and recurrence quantification analysis (RQA). PFs and AE signals of a lab‐scale fluidized bed were measured simultaneously at various superficial gas velocities and mean particle sizes. Although the AE signals are often very complicated due to many different acoustic sources in the bed, applying RP analyses showed that small changes in mean particle size can be detected by visual comparison of AE‐RP structures, while this cannot be distinguished by graphical RP analysis of PFs. Moreover, the hydrodynamics of the bed was inspected through RQA analysis of both signals. For this purpose, recurrence rate, determinism, laminarity, average length of diagonal and vertical lines were extracted from RPs showing the effect of an increase in the mean particle size. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3547–3561, 2016     

Sensitivity analysis of the OWA operator.

The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for the OWA. These two methods will be first analyzed in detail by using sensitivity analysis on the outputs of the OWA with respect to the optimism degree of the decision maker, and then the two methods will be compared. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree, and, therefore, it has better computation efficiency. Since maximizing the combined goodness measure and minimizing its sensitivity to optimism degree are conflicting objectives, a new composite measure of goodness will be defined to have more reliability in obtaining optimal solutions. The theoretical results will be illustrated in a water resources management problem.     

Fuzzy quantifiers in sensitivity analysis of OWA operator

The efficient use of the Ordered Weighted Averaging (OWA) operator in decision making problems depends on the choice of the order weights. Using fuzzy quantifiers is one of the most popular methods to obtain them. In this study, a new method will be introduced for determining the order weights from the quantifiers, which is especially useful in the case of unimodal quantifiers. The new method is generic and has better computational efficiency in comparison to the previously applied methods. In addition, a new measure for sensitivity analysis on the outputs of OWA operator will be introduced. The theoretical results will be illustrated by a Ph.D. student selection problem discussed earlier in the literature.     

Urban Water Management Using Fuzzy-Probabilistic Multi-Objective Programming with Dynamic Efficiency

This paper introduces a new multi-objective optimization model for integrated urban water management. The model, based on compromise programming, is applied for the case of Tabriz city in Iran. The water demand of this city is rapidly growing and because of the limited resources, water supply is now more vulnerable to any mismanagement. Therefore the model attempts to optimize the water supply plan of city concerning three main objectives of maximizing the water supply, minimizing the cost and minimizing the environmental hazards. Due to the vagueness in defining the first objective, it is modeled by using the fuzzy set theory. Further, the uncertainty in satisfying some constraints is tackled by using the chance constraint approach. The decision variables are the extent of water withdrawal from the city aquifer, three different water transfer schemes and also the extent of demand management by leaks detection and pipes rehabilitation. Then the fuzzy-probabilistic multi-objective model is solved by considering the new idea of dynamic efficiency in the utility of decision maker and the results provide the optimum water supply in the planning horizon. The model results in robust solutions in which the demand management option dominates the new water transfer. Implementing the results of this model supports the environmental conservation and sustainable development.