Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

Minimum cost consensus model for CRP-driven preference optimization analysis in large-scale group decision making using Louvain algorithm

Large-scale group decision-making problems based on social network analysis and minimum cost consensus models (MCCMs) have recently attracted considerable attention. However, few studies have combined them to form a complete decision-making system. Accordingly, we define the satisfaction index to optimize the classical MCCM by considering the effect of the group on individuals. Similarly, we define the consistency index to optimize the consensus reaching process (CRP). Regarding the evolution of the consensus network, the Louvain algorithm is used to divide the entire group into several subgroups to ensure that each subgroup is independent but has strong cohesion. By constructing the MCCM based on the satisfaction index and the optimized consensus-reaching process, the group opinions in each subgroup are ranked to obtain the final ranking of alternatives. Finally, to verify the validity of CRP and the practical value of the proposed model, we conduct consensus network evolution and decision-making analysis in the case of a negotiation between the government and polluting companies to achieve uniform pollution emissions. Sensitivity analysis is performed to demonstrate the stability of the subgroup weights. Furthermore, a comparative analysis using existing models verifies the effectiveness of the proposed model.     

MDA-TOEPGA: A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention. Until now, existing methods for identifying miRNA related disease mainly rely on top-ranked association model, which may not provide a full landscape of association between miRNA and disease. Hence there is strong need of new computational method to identify the associations from miRNA group view. In this paper, we proposed a framework, MDA-TOEPGA, to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm, which identifies latent miRNAdisease associations from the view of functional module. To understand the miRNA functional module in diseases, the case study is presented. We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm. Experimental results showed that our method cannot only outperform classical algorithms, such as K-means, IK-means, MCODE, HC-PIN, and ClusterONE, but can also achieve an ideal overall performance in terms of a composite score consisting of f1, Sensitivity, and Accuracy. Altogether, our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.     

Integrated process design,scheduling, and model predictive control of batch processes with closed-loop implementation

Simultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed-integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed-loop implementation. We use multi-parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base-2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.     

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.     

Shale gas pad development planning under price uncertainty

In this article, we study shale gas pad development under natural gas price uncertainty. We optimize the sequence of operations, gas curtailment, and storage on a single pad to maximize the net present value. The optimization problem is formulated as an mixed-integer linear programming model, which is similar to the one proposed by Ondeck et al. We investigate how natural gas price uncertainty affects the operation strategy in the pad development. Both two-stage and multistage stochastic programming are used as the mathematical framework to hedge against uncertainty. Our case study shows that there is value of using stochastic programming when the price variance is high. However, when the variance of the price is low, solving the stochastic programming problems does not create additional value compared with solving the deterministic problem.     

High-speed,two-dimensional digital image correlation algorithm using heterogeneous (CPU-GPU) framework

Two-dimensional digital image correlation (2D-DIC) is an experimental technique used to measure in-plane displacement of a test specimen. Real-time measurement of full-field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU-based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU-GPU) framework in combination with a pyramid-based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by compared to a sequential CPU-based implementation for a pair of gray-scale images with a resolution of pixels. As an important milestone, feasibility to measure deformations in real time ( 1 s) is manifested in this study.     

Offshore manufacturing contract design based on transfer price considering green tax: a bilevel programming approach

The problem of designing offshore manufacturing contract resulting in optimal transfer price is troubling multinational companies over the past few years. This paper proposes designing offshore manufacturing contracts based on the transfer price in the form of bilevel programming problems after considering green tax. In these contract designs, a firm in a developed country sells a single product in its market. The same product is simultaneously being manufactured by another firm in a developing country with lower manufacturing cost. After anticipating the consumer demand, the seller places an order, based on which the manufacturer manufactures the ordered quantity, and offers a transfer price which in turn maximises its net profit after paying green tax to its government. While setting the transfer price, the manufacturer considers the manufacturing cost, the export duty payable to its government and the cost of shipping the product to the developed country. After buying the product from the manufacturer at the transfer price, the seller then sets the retail price which maximises its net profit after paying the import duty to its government; the retail price, however, must not be more than the maximum retail price applicable to the market. Thus, offshore manufacturing contract results in optimal after-tax profits for both the firms. An experimental study has been carried out to discuss the practical aspects of the results developed, where a US firm is offshoring its manufacturing activity to a Chinese firm in order to draw maximum profit.     

Robust model for optimization of forming process for metallic bipolar plates of cleaner energy production system

Energy production systems such as proton-exchange membrane fuel cell (PEMFC) has a promising future in the cleaner energy market due to zero emissions. Rubber pad forming (RPF) process of metallic bipolar plates of PEMFCs is gaining attention among the researchers. Studies based on design of experiments have been conducted to find the crucial parameters of the forming process. These methods are based on the assumptions of the model structure, correlated residuals, etc., which can cause uncertainty in estimation ability of the model on unseen data. Therefore, the present study focuses on the design of robust models of these parameters for PEMFCs using an optimization approach of genetic programming (GP). The inputs from the experiments considered in GP are radius, the friction coefficient, the filling factor and the minimum thickness. Experiments on PEMFCs validates the performance of the GP models. Further, the relationships between the two inputs and the three outputs for PEMFCs are generated as well as the contributions of each input to each of the output. Optimization of the models generated by GP can further determine the forming quality of metallic bipolar plates of PEMFCs by an appropriate setting of the two inputs.     

Experimental and optimization of material synthesis process parameters for improving capacity of lithium‐ion battery

New methods for synthesis of active materials have been developed to improve capacity and cycle life performance of lithium‐ion batteries. Past studies have focused on routes of development of materials and new processes, which might not be economical for large‐scale production. In this regard, this study examines a widely employed carbothermal reduction technology for the synthesis of lithium‐iron phosphate (LiFePO₄/C) and investigates effects of process conditions during this synthesis on final battery performance. An experimental combined genetic programming approach is used to model the effects of crucial process conditions (sintering time, the carbon content, and the sintering temperature) on the discharge capacity of the assembled battery. Experiments are conducted to collect the discharge capacity data based on varying LiFePO₄/C synthesis conditions, and genetic programming is employed to develop a suitable functional relationship between them. The results show that the battery discharge capacity is controlled significantly by adjusting sintering temperature and carbon content, while the effect of sintering time is found to be insignificant. Further, the interaction effect of the sintering time and carbon content is much more obvious than that of the sintering time and the sintering temperature. The findings from the study pave the way for the optimum design of the synthesis process of LiFePO₄/C for a higher battery performance.