A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

INTELLIGENT CONTROL SYSTEM FOR STREAM SAMPLING

This article introduces an intelligent sampling controller to assist the actions taken by a plant operator to correct sampling conditions. This hybrid system includes a sampling error filter (SEF), a sampling performance indexer (SPI), a sampling correctness inspector (SCI), and a sampling error evaluator (SEE). First, the SEF upgrades the measured variables in a mineral-processing system by material balance. Then, the SPI employs fuzzy logic to assess the sampling performance. In addition, the expert system SCI checks the correctness of the sampling process. Finally, the sampling error is computed by the expert system SEE.     

KNOWLEDGE-BASED ASSESSMENT OF STREAM SAMPLING

This article presents two novel knowledge-based systems for stream-sampling assessment. The first expert system inspects the stream-sampling correctness in mineral-processing plants. It is called the sampling correctness inspector. The second expert system evaluates sampling errors in mineral-processing plants. It is called the sampling error evaluator. The knowledge of both expert systems is collected from the authors' expertise, in addition to other experts' knowledge of sampling mineral-processing streams. These knowledge-based systems take into account the stream properties, the cutter features, and the sampling manner. They were developed and tested successfully.     

FUZZY EVALUATION OF STREAM SAMPLE RELIABILITY

The operation of a mineral-processing plant is investigated regularly by stream sampling. Sampling errors are generated due to material heterogeneity and cutter features. This article presents an innovative method using fuzzy logic to assess the sample reliability, which is assumed to be influenced by the cutter geometry, speed, layout, and path. Fuzzy logic permits sampling situations to be described and processed in linguistic terms such as very reliable, reliable, adequate, doubtful, and very doubtful. Each sample is assigned a sampling performance index. This is the basis for decision-making concerning sampling strategy and the weighting factors in data-reconciliation techniques.     

MATERIAL BALANCE AND STREAM SAMPLING ERRORS

This article investigates the influence of the data variation on sampling errors throughout a two-stage flotation circuit. The material balance technique is used to upgrade the measured variables. The sampling errors are evaluated before and after material balance. In one case, the covariance terms between the components in a stream and between the streams in the flotation circuit are included. In the other case, these terms are not included. This article proves that by including the covariance terms in the calculation, the variances of the sampling errors are reduced and, therefore, the reliability of the material balance is improved.     

Electrochemical and structural study of Ce0.8Sm0.2−xLaxO1.9 electrolyte materials for SOFC

Ce_0.8Sm_0.2−xLa_xO_1.9 powders, denoted as La_xSDC (for x=0, 0.01, 0.03, 0.05, 0.07 and 0.1), were synthesized via the mechanical milling reaction method. The La³⁺ doping content has a remarkable influence on structural and electrical properties. The phase identification and morphology were studied by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Lattice parameters were calculated by the Rietveld method. It was observed that the lattice parameter values in Ce_0.8Sm_0.2−xLa_xO_1.9 systems obey Vegard's law. The pellets were then sintered at 1500 °C in air for 7 h. The relative densities of these pellets were over 93.7%.The electrical conductivity was studied using two-probe impedance spectroscopy and results showed that the conductivity of Ce_0.8Sm_0.2−xLa_xO_1.9 first increased and then decreased with La dopant content x. Results also showed that Ce_0.8Sm_0.17La_0.03O_1.9 had the highest electrical conductivity, σ₇₀₀ °C equal to 3.8×10⁻² Scm⁻¹ and an activation energy equal to 0.77 eV. It was therefore concluded that co-doping with the appropriate amount of La can further improve the electrical properties of ceria electrolytes.     

STREAM MATERIAL VARIABLES AND SAMPLING ERRORS

Stream sampling is necessary to assess and control a mineral-processing plant operation properly. However, the sampling process is always marred by errors. Therefore, it is paramount to evaluate the sampling errors correctly. This article studies the influence of independent composition and mass flow rate variations on stream sampling errors, which is a novel approach. The value of each variable series at a certain time depends on a random disturbance. In this case, the various mineral-processing variables are described by time series models. This article shows that the samples collected from streams with higher autocorrelations are more representative.     

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.     

Synthesis and electrical properties of co-doping with La3+, Nd3+, Y3+, and Eu3+ citric acid-nitrate prepared samarium-doped ceria ceramics

Fluorite oxides Ce_0.8Sm_0.1Ln_0.1O_1.9 (denoted as SDC for singular doping and LnSDC for Ln=La, Nd, Y and Eu), were prepared by the citric acid–nitrate combustion reaction to act as electrolytes for intermediate-temperature solid oxide fuel cells (IT-SOFC). The thermal decomposition, phase identification, morphology, density, particle size distribution and electrical properties of the samples were studied by TGA/TDA, XRD, SEM, the Archimedes method, a laser size analyzer and Impedance spectroscopy, respectively. All crystallite powders that calcined at 800 °C had a cubic fluorite structure; the average crystallite size was between 63 and 68.5 nm. The pellets were then sintered at 1400 °C in air for 7 h. The relative densities of these pellets were over 95%, which was in good agreement with the results of the SEM. The impedance measurements were performed in an open circuit using two electrode configurations. The results showed that Ce_0.8Sm_0.1La_0.1O_1.9 had the highest electrical conductivity, σ_700 °C, equal to 6.59×10^?2 S cm^?1 and the lowest activation energy equal to 0.85 eV. It was therefore concluded that co-doping with the appropriate rare-earth cations can further improve the electrical properties of ceria electrolytes.