Reliability Objective Function Research Articles

Runoff Prediction of Tunxi Basin under Projected Climate Changes Based on Lumped Hydrological Models with Various Model Parameter Optimization Strategies

Runoff is greatly influenced by changes in climate conditions. Predicting runoff and analyzing its variations under future climates are crucial for ensuring water security, managing water resources effectively, and promoting sustainable development within the catchment area. As the key step in runoff modeling, the calibration of hydrological model parameters plays an important role in models’ performance. Identifying an efficient and reliable optimization algorithm and objective function continues to be a significant challenge in applying hydrological models. This study selected new algorithms, including the strategic random search (SRS) and sparrow search algorithm (SSA) used in hydrology, gold rush optimizer (GRO) and snow ablation optimizer (SAO) not used in hydrology, and classical algorithms, i.e., shuffling complex evolution (SCE-UA) and particle swarm optimization (PSO), to calibrate the two-parameter monthly water balance model (TWBM), abcd, and HYMOD model under the four objective functions of the Kling–Gupta efficiency (KGE) variant based on knowable moments (KMoments) and considering the high and low flows (HiLo) for monthly runoff simulation and future runoff prediction in Tunxi basin, China. Furthermore, the identified algorithm and objective function scenario with the best performance were applied for runoff prediction under climate change projections. The results show that the abcd model has the best performance, followed by the HYMOD and TWBM models, and the rank of model stability is abcd > TWBM > HYMOD with the change of algorithms, objective functions, and contributing calibration years in the history period. The KMoments based on KGE can play a positive role in the model calibration, while the effect of adding the HiLo is unstable. The SRS algorithm exhibits a faster, more stable, and more efficient search than the others in hydrological model calibration. The runoff obtained from the optimal model showed a decrease in the future monthly runoff compared to the reference period under all SSP scenarios. In addition, the distribution of monthly runoff changed, with the monthly maximum runoff changing from June to May. Decreases in the monthly simulated runoff mainly occurred from February to July (10.9–56.1%). These findings may be helpful for the determination of model parameter calibration strategies, thus improving the accuracy and efficiency of hydrological modeling for runoff prediction.

A Conceptual model of mixed-integer linear programming water distribution system

Water is a basic part of our daily lives, as such effective water supply is of paramount importance. Thus, as a result of the rise in population size and water shortage there is the need for proper, suitable and optimal utilization of water resources to efficiently be distributed among the populace. The proper allocation and distribution of water in the field of network planning need to be modelled through mathematical parameters for objective of water distribution system. This mathematical approach requires of solving an optimization problem based on multi-objective function subjected to certain constraints of mixed integer linear programming objective function which is proportional to the cost of the water distribution network. This study present a conceptual model of multi-objective optimization proposed for determination of design parameters of water distribution system by considering the significant number of constraints, decision variables, cost and reliability objective functions. The model was proposed to solve the reliability problem of water production and reduce the design and operational costs.

Experimental Evaluation of the Mobility Profile of Enhanced Oil Recovery Gases

The mobility profiles of gases used in enhanced oil recovery (EOR) have been thoroughly investigated through the coupling operations of data mining of oilfield data and experimental data analyses. Mobility as an EOR objective function has not been previously applied to characterize potential reservoirs for EOR selection and application, even though it is a robust combinatorial function that benefits from two petrophysical variables, permeability and viscosity. The data mining approach identified mobility as a reliable objective function for reservoir characterisation. The data distribution and clustering results indicate that Gas EOR reservoirs have relatively higher mean mobility than Thermal, Microbial and Chemical EOR reservoirs. The experimental approach investigated EOR gases, CO2, CH4, N2, and Air. A modified Darcy Equation of State for gas flow through porous media was applied to evaluate which gas would competitively attain the oil displacement optimisation criterion for mobility ratio, M ≤ 1. Coupling the data mining with the experimental data results reveals that gas reservoirs can be further categorized by mobility. CH4 (18.16 mD/cp) was observed to have the highest mobility followed by Air (14.60 mD/cp), N2 (13.61 mD/cp), and CO2 (12.96 mD/cp). The gas mobility order significantly corresponds with the mobility distribution of reservoirs that implemented gas EOR processes. It was concluded that CO2 offers relatively lower mobility, therefore, it is the most competitive EOR gas to approach the mobility ratio criterion of unity or less.

Game theory for multi-objective and multi-period framework generation expansion planning in deregulated markets

A model that can optimize the generation expansion planning in deregulated market by using economic and reliability objective function is needed to meet the requirements of current expansion planning. An optimization model based on economic and reliability objective functions is the purpose in this research. To achieve this goal, this research combines multi-objective function and multi-period framework into game theory. The reliability objective function represented by limitation function, while the economic objective function is a function to find the minimum cost. Therefore, both functions cannot be combined, so that a bi-level optimization method is needed to solve the problem. To find the optimum solution, it uses probability values of a mixed strategy. The values in a mixed strategy are calculated using sequential quadratic program-based quasi-newton method. The optimum solution is the strategy that has the greatest probability values. Reliability Test System is used as the case study. The optimization results show that the game theory multi-period framework multi-objective function can be used and produce optimum planning. This is indicated by the total levelized cost in this research. The cost is 2.96% smaller than the benchmark. All of the reliability indices are still within the standard limit.

GNSS Monitoring Network Optimization Case Study: Opak Fault Deformation, Yogyakarta

Opak fault is a fault located in Opak River area, Bantul. The existence of the fault is one of the biggest causes of earthquake in Yogyakarta in 2006. The seismic potential caused by the active fault requires continuous geodynamic monitoring. The GNSS network (TGD, SGY, and OPK) have been developed since 2013 consists of 17 stations and in 2016 there was an additional number of four monitoring stations. Several high-precision monitoring stations distributed at the fault location are needed to monitor the fault movement. Optimal observation network is one of the factors to obtain high precision station coordinates. The GNSS network optimization has been carried out in the previous research partially on each network; namely the segment of TGD, SGY, and OPK. Therefore, this research conducts a thoroughly optimization for 17 monitoring stations either use old or new stations to obtain an optimal network based on the criteria of accuracy and reliability.The network is designed widely from simple to complex combination and to combination between network segments. The computation uses least squares adjustment with parameter method. The value of the cofactor matrix parameter of the adjustment is applied to analyze the network based on the function of the accuracy criteria, namely A-Optimality, D-Optimality, E-Optimality, S-Optimality, and I-Optimality. Meanwhile, the value of the residual cofactor matrix is used for network configuration analysis based on the reliability objective function, namely the individual redundancy, external and internal reliabilities criteria. The result showed that the design of TGD, SGY and OPK network segments are optimized based on the criteria of accuracy and reliability if they use a network design with a complex baseline. The criteria for accuracy and reliability in the design with a combination of segments such as TGD and SGY, TGD and OPK, as well as TGD, SGY, and OPK are not much different from the optimization results performed by each segment. Therefore, if the measurements are carried out with a limited receiver, it is better to use each of segment designs.

New strategies for estimation of cut and fill areas in road design with different ground cross-section offsets

ABSTRACTEarthwork operations accounted for great amount of forest road construction costs. Any Optimization in planning road profile needs a reliable objective function that calculates earthwork volume in an appropriate time and accuracy. This study aimed at incorporating the shape of the ground cross sections in calculating cut/fill areas using three alternative methods including: Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and heuristic programming in MATLAB environment. The accuracy of results was validated via AutoCAD as a manual method for area calculation of road’s cross-section. Trapezoidal method applied to calculate the area of cut/fill in the heuristic method. Paired sample T-Test and statistical analysis (ANOVA) with confidence level of 0.95 indicated that the heuristic method not only reduced computing time but also provided accurate estimations as same as the manual AutoCAD based method. It can be concluded that although MLR and ANN estimations were as well as AutoCAD results within a reasonable time, they need to be retrained by adding, removing or changing entrance parameters.

System redundancy optimization with uncertain stress-based component reliability: Minimization of regret

System reliability design optimization models have been developed for systems exposed to changing and diverse stress and usage conditions. Uncertainty is addressed through defining a future operating environment where component stresses have shifted or changed for different future usage scenarios. Due to unplanned variations or changing environments and operating stresses, component and system reliability often cannot be predicted or estimated without uncertainty. Component reliability can vary due to a relative increase/decrease of stresses or operating conditions. The uncertain parameters of stresses have been incorporated directly into the new decision-making model. Risk analysis perspectives, including risk-neutral and risk-averse, are considered as system reliability objective functions. A regret function is defined, and minimization of the maximum regret provides an objective function based on random future usage stresses. This is an entirely new formulation of the redundancy allocation problem, but it is a relevant one for some problem domains. The redundancy allocation problem is solved to select the best design solution when there are multiple choices of components and system-level constraints. Nonlinear programming and a neighborhood search heuristic method are recommended to obtain the integer solutions for risk-based formulations.

Метод декодування лінійних блокових кодів на основі популяційних процедур пошукової оптимізації

It is shown that the classical methods of decoding linear block codes are based on the solution of algebraic equations and provide hard decisions. Thus, these decoding methods have low correction ability so do not satisfy the requirements of modern telecommunications systems. It is shown that the lack of non-algebraic decoding methods of linear block codes for ordered statistics is relatively high computational complexity, which makes it suitable only for short length codes. The necessity to improve the efficiency of decoding linear block codes used in modern telecommunications systems has been grounded. The variants of representing the problem of linear block codes decoding depending on the communication channel model have been considered. It is shown that this problem is a problem of discrete optimization, the dimension of which can be reduced by using the most reliable basis based on information about the reliability of the elements of received vector. The objective function which is advisable to used in the decoding of linear block codes is presented. Decoding method of linear block codes based on the joint use of the most reliable basis, objective function and population procedures of search optimization has been proposed

An intelligent approach for optimal capacitor placement problem as a reliability reinforcement strategy

This paper proposes a new method based Cuckoo Search Algorithm (CSA) to investigate the optimal capacitor placement and sizing from the reliability point of view. In this regard, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI) as the customer-orientated indices and Average Energy Not Supplied (AENS) as the load and energy-orientated index are utilized as the reliability objective functions. Meanwhile, to reach a compromise between the original objective functions and reliability ones, the total system cost including the MW cost of power losses as well as the capacitor investment cost is also considered as an objective function. In order to handle the proposed nonlinear constraint multi-objective optimization problem suitably, a new self adaptive modification method based on Cuckoo Search Algorithm (CSA) is proposed. The feasibility and effectiveness of the proposed method is investigated through a standard test system.

Optimal Reliability Path Problem on Networks with Random and Correlated Link Travel Times

This study addressed the most reliable path problem on stochastic networks with correlated link travel times from general multivariate distributions. Both evaluation and optimization of this problem are difficult because of the absence of closed-form distribution at the path level. For tractability in reliability evaluation, the path reliability objective function was substituted by the standard-score objective with the same mean and variance as the given distribution. However, this simplified proxy objective was difficult to optimize because it was nonlinear in path mean and variance. These difficulties were circumvented by reformulating the problem as a bilevel minimization model, in which the master problem corresponded to the proxy reliability objective, and the subproblem corresponded to the solution of a related minimum robust-cost path problem. The subproblem was easier to solve because it minimized a linear combination of path travel time mean and standard deviation. An effi-cient algorithm was proposed to solve this bilevel model, whose exact and monotonic convergence was established for the proxy objective function. Computational experiments on various test networks demonstrated the robustness of the algorithm under various travel time distributions. The algorithm identified the optimal path in 96% to 99% of the cases with a maximum error of less than 0.5%. The algorithm was also applied with real-world data from an intelligent transportation systems test bed in Chennai, India. The results showed that the most reliable paths varied within the day and from day to day; this result showed potential applications of the algorithm with advanced traveler information systems.

Research on Reliability Allocation Method for NC Grinding Machine

Reliability allocation is a collection problem of engineering decision, multiple factors comprehensive balance and process optimization. Considering that both the factors that influenced the reliability allocation and their weights have the characteristics of uncertainty, which always be ignored .Therefore a subjective and objective combined weights method that combined the entropy method and Interval analysis method was proposed. Taking NC grinding machine as an example, firstly, establish the reliability objective function based on the minimum discrimination information principle, and employ the Lagrange multiplier to get the combined weights of the 6 factors. Then use the fuzzy comprehensive allocation method to obtain the reliability allocation results, which was compared with the actual accumulated data, so that the feasibility and effectiveness of this method was verified.

MicroGrid Operation and Design Optimization With Synthetic Wins and Solar Resources

Microgrids have been significantly developed, enhanced by concerns about climate change and energy security, their decreasing costs and the development of renewable energy sources. However, an important concern is the limited information available to estimate these renewable resources. We develop an optimization model with cost and reliability objective functions for the design and operation of micro-networks using a nested strategy and limited resource information. Design optimization utilizes Genetic Algorithms and 2 objective functions: Expected Energy Not Supplied EENS and Levelized Cost of Energy. In addition, Green House Gas (GHG) emissions are estimated. Operational optimization utilizes Generating Sets Search Algorithm. We include models for wind turbines, solar panels, fuel cells, diesel generators, gas turbines, and battery banks. We address the limited data available for these applications by synthesizing series of wind and solar radiation with basic statistical parameters. Pareto-Optimal trade-off curves between cost and reliability are presented here for an example network.

Design of active and stable NiCeO2ZrO2MgAl2O4 dry reforming catalysts

An active and stable NiCeO2ZrO2MgAl2O4 catalyst for dry reforming in severe coking conditions (CO2/CH4=1.1, GHSV=32,000h−1, 670°C and 1.5bar) was developed by combining conventional and combinatorial research strategies using high-throughput experimentation. A D-optimal design was applied to setup statistically verified models for catalytic activity (XCH4,XCO2), selectivity (H2/CO molar ratio) and stability (coke resistance) as function of the selected input factors, viz. the amount of active element and amount of promoters. The importance of selecting the proper objective function R and appropriate unit to express the amount of coke deposited are discussed in detail. Via the most reliable objective function R an optimal catalyst composition area is obtained, where catalysts combine high activity with a long service lifetime. It was recognized that stable dry reforming occurs only with a subtle balance of the catalyst composition. Best dry reforming performances are achieved with catalyst compositions having low Ni (2wt%), ZrO2 (<1wt%) and high CeO2 (>3wt%) content.

Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions

One way to reduce excessive fuel consumption and vehicular emissions on urban streets is to optimize signal timings. Historically, signal timing optimization tools were used to reduce traffic delay and stops. The concept of optimizing signal timings to reduce fuel consumption and emissions was addressed decades ago with tools that are now considered outdated. This study advocates a fresh approach to integrating existing state-of-the-art tools for reassessing and ultimately minimizing fuel consumption and emissions. VISSIM, CMEM, and VISGAOST were linked to optimize signal timings and minimize fuel consumption and CO2 emissions. As a case study, a 14-intersection network in Park City, Utah, was used. Signal timings were optimized for seven optimization objective functions to find the lowest fuel consumption and CO2 emissions. Findings show that a formula commonly used to estimate fuel consumption in traffic simulation tools inadequately estimates fuel consumption and cannot be used as a reliable objective function in signal timing optimizations. Some of the performance measures used as objective functions in the optimization process were proved to be ineffective. When CMEM-estimated fuel consumption is used as an objective function, estimated fuel savings are around 1.5%, a statistically significant decrease. Further research is needed to find an effective way to minimize fuel consumption and emissions by using the proposed approach.

Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.

A word-oriented approach to alignment validation

Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences. Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families. On request from the authors.

Towards a reliable objective function for multiple sequence alignments

Multiple sequence alignment is a fundamental tool in a number of different domains in modern molecular biology, including functional and evolutionary studies of a protein family. Multiple alignments also play an essential role in the new integrated systems for genome annotation and analysis. Thus, the development of new multiple alignment scores and statistics is essential, in the spirit of the work dedicated to the evaluation of pairwise sequence alignments for database searching techniques. We present here norMD, a new objective scoring function for multiple sequence alignments. NorMD combines the advantages of the column-scoring techniques with the sensitivity of methods incorporating residue similarity scores. In addition, norMD incorporates ab initio sequence information, such as the number, length and similarity of the sequences to be aligned. The sensitivity and reliability of the norMD objective function is demonstrated using structural alignments in the SCOP and BAliBASE databases. The norMD scores are then applied to the multiple alignments of the complete sequences (MACS) detected by BlastP with E-value<10, for a set of 734 hypothetical proteins encoded by the Vibrio cholerae genome. Unrelated or badly aligned sequences were automatically removed from the MACS, leaving a high-quality multiple alignment which could be reliably exploited in a subsequent functional and/or structural annotation process. After removal of unreliable sequences, 176 (24 %) of the alignments contained at least one sequence with a functional annotation. 103 of these new matches were supported by significant hits to the Interpro domain and motif database.

A Practical Model for Generator Maintenance Scheduling with Transmission Constraints

In this paper an optimal maintenance scheduling of generating units in a power system has been developed with transmission network representation. Here a DC load flow has been embedded in the maintenance model to include network constraints resulting in a more practical maintenance schedule. The model developed here uses the minimization of system cost (production cost plus the unserved energy cost) as the objective criterion, whereas the reliability objective function used is the minimization of unserved energy. The optimization is achieved by integer linear programming. The incorporation of transmission network adds significant complexity to maintenance scheduling. The proposed model enables almost all practical maintenance scheduling constraints to be handled easily. The optimization has been carried out to minimize the cost function considering different cases (i.e., with and without incorporation of the transmission network). The effectiveness of the proposed method has been demonstrated by obtaining numerical results on sample and real scale test systems. A comparison of the cost objective function clearly indicates that the maintenance schedule obtained from the simple generation model alone is more expensive than the one with transmission, and that there is a considerable degree of suboptimality in the former case.