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[Objective] Taking the separation of isobutene/isobutane by conventional and side-stream extractive distillation as the research object, the optimization design of conventional and side-stream extractive distillation was conducted based on decision variables. [Methods] A multi-objective evaluation was conducted from the perspectives of energy, economy, thermodynamics, and environment. The optimal operating parameters for the conventional and side-stream extractive distillation and solvent recovery columns were obtained through the optimization of decision variables. Two extractive distillation processes were optimized with TAC as the target, and the optimal feed stage, extractant feed stage, and number of theoretical and sideline extraction stages were obtained. [Results] The TAC of the optimized conventional and side-stream extractive distillation was 3.26 x 106 and 3.1 x 106 $/year, their TEC was 14 041.9 and 13 717.87 kW, and their CO2 emissions were 4 831 and 4 714 kg/h, respectively. Compared with those of the conventional extractive distillation process, the CO2 emissions, TAC, and TEC of the side-stream extractive distillation were reduced by 2.4%, 5.2%, and 2.4%, respectively. The thermodynamic efficiency increased from 22.4% of the conventional extractive distillation to 23.2% of the side-stream extractive distillation. These results show that, in terms of economic and environmental benefits, the side-stream extractive distillation had greater advantages than the conventional extractive distillation. With two extractive distillation design schemes, students' innovative thinking is stimulated, and new distillation technologies and methods are explored. [Conclusions] Through multi-objective evaluation methods, students' practical ability in distillation operations and awareness of energy saving and consumption reduction are cultivated, enhancing their awareness of energy efficiency and sustainable development concepts, which is conducive to improving their enthusiasm for learning and strengthening their innovative ability in engineering practice. [ABSTRACT FROM AUTHOR]

Due to the different complementarity and compatibility of various components in the wind‐solar storage combined power generation system, its energy storage complementary control is very important. In order to ensure the stable operation of the system, an energy storage complementary control method for wind‐solar storage combined power generation system under opportunity constraints is proposed. The wind power output value is obtained. Aiming at the maximum similarity between the total output of wind power storage and the planned output curve, combined with the opportunity constraints and the output and electric quantity constraints of energy storage devices, the energy storage complementary control model is established, and the model is solved by improving the adaptive particle swarm optimization algorithm. The experimental results show that the total output of the wind‐solar storage combined power generation system is consistent with the expected output, and the utilization rate of wind‐solar resources is effectively improved. It has good convergence performance, and can better meet the needs of actual power work. [ABSTRACT FROM AUTHOR]

Due to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives. [ABSTRACT FROM AUTHOR]

This study investigated the use of public and private healthcare systems separately and interactively to find solutions to various problems the healthcare sector has faced including waiting time, cost, and quality of service. Patients, private and public healthcare providers, funders, and governments that make rules about healthcare were defined as components of healthcare systems in this study. Mathematical optimization models were developed by deriving scenarios to balance the components of healthcare systems. In addition to derivation of scenarios related to types of healthcare systems and decision variables, including copayment, treatment cost, reimbursement, and premium, numerical studies for the healthcare components were performed to determine which healthcare systems were useful. The minimum fee payable by patients for the easy access to healthcare schemes was calculated. While there was no remarkable change in the waiting time of the patients in the private healthcare system, the waiting time were reduced by 48.70% in the public healthcare system among the findings. The amount of reimbursement decreased significantly as a result of the interaction of healthcare systems. By reducing the amount of co-payment to the amount charged by the public healthcare system, it has been ensured that private healthcare providers treat more patients. Based on computational analyses, because of the agreements provided between the components, we have advocated that public and private healthcare systems must be unified to provide greater benefits for all the components of healthcare. [ABSTRACT FROM AUTHOR]

Integration of renewable energies such as wind and solar with an energy storage system (ESS) in a distribution network is the interest of current studies in power system engineering. Wind and battery ESS (BESS) are known for their complement and efficient approaches into the distribution networks. The promising of renewable energies for wind and solar in Afghanistan is a motivation for stepping up the power sector of the country by enhancing the power quality as well as self‐dependency in electricity production. In this study, a multi‐objective optimisation technique, non‐dominated sorting genetic algorithm II (NSGA‐II) is proposed for an extensive distribution network in Kabul city considering technical, environmental, and financial control schemes for the network improvement. Three different scenarios with various objective functions are deemed to evaluate their impact on decision variables and network parameters. Furthermore, optimum allocation of the wind turbine and charge/discharge scheduling of BESS are revealed with improvement in performance of the power system. Simulations are deployed in MATLAB® with its application on developed 162‐bus real‐distribution network to demonstrate the effect of different objective function arrangements in each scenario as well as confirming the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: Clinical training programs increasingly use entrustable professional activities (EPAs) as focus of assessment. However, questions remain about which information should ground decisions to trust learners. This qualitative study aimed to identify decision variables in the workplace that clinical teachers find relevant in the elaboration of the entrustment decision processes. The findings can substantiate entrustment decision-making in the clinical workplace.Methods: Focus groups were conducted with medical and veterinary clinical teachers, using the structured consensus method of the Nominal Group Technique to generate decision variables. A ranking was made based on a relevance score assigned by the clinical teachers to the different decision variables. Field notes, audio recordings and flip chart lists were analyzed and subsequently translated and, as a form of axial coding, merged into one list, combining the decision variables that were similar in their meaning.Results: A list of 11 and 17 decision variables were acknowledged as relevant by the medical and veterinary teacher groups, respectively. The focus groups yielded 21 unique decision variables that were considered relevant to inform readiness to perform a clinical task on a designated level of supervision. The decision variables consisted of skills, generic qualities, characteristics, previous performance or other information. We were able to group the decision variables into five categories: ability, humility, integrity, reliability and adequate exposure.Discussion: To entrust a learner to perform a task at a specific level of supervision, a supervisor needs information to support such a judgement. This trust cannot be credited on a single case at a single moment of assessment, but requires different variables and multiple sources of information. This study provides an overview of decision variables giving evidence to justify the multifactorial process of making an entrustment decision. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present an iterative method to assist the designer in the setting of decision variables in an optimization problem with analytical models. This method is based on the Design Structure Matrix (DSM) which allows a clear representation of interactions between variables. Such a process becomes particularly useful for complex optimal design for which choosing the right decision variables to efficiently solve the problem is not so trivial. This approach is applied to the geometrical model of a High Speed Permanent Magnet Synchronous Machine (HSPMSM). [ABSTRACT FROM AUTHOR]

Accurate estimation and reliable universal performance of reference evapotranspiration (ET0) obtained from a few meteorological parameters are important for the rational planning of agricultural water resources and the effective management of water in irrigated regions. Meteorological data in southern China were used to calculate ET0 using the standard Penman-Monteith formula and determined the core decision variable (hours of sunshine, N) and the limited decision variable (relative humidity, RH) using path analysis. Estimation models using an artificial neural network and wavelet neural network were established for the Wuhan and Guangzhou meteorological stations. The statistical indices were positively correlated with the decision contribution rates to ET0. The ET0 values for other stations in southern China were all estimated by these models, which were trained for the Guangzhou station, and then made a total comparison with Hargreaves-Samani (HS) and Priestley-Taylor (PT) empirical ET0 models. Error analysis indicated that the root mean square error and the mean absolute per cent error were around 0.32 mm and 5.5%, respectively, with a high coefficient of determination and Nash-Sutcliffe efficiency over 0.9, indicating that these estimating models could be applied in more regions for universal analysis with high accuracy. [ABSTRACT FROM AUTHOR]

Today, rail transport systems are widely used in the world. Because of the high consumption of energy in these systems, finding ways to optimize their energy consumption is important. One of the best ways to save more energy and prevent the losses of rail transportation is using the optimal speed profile. In this article, intelligent algorithms, involving ant colony optimization for continuous domain (ACOR) and genetic algorithm, are applied to the energy efficiency problem of electrical trains for various track gradients and curvatures. With proper determination of switching points in the moving strategies such as acceleration, cruising, and coasting, the optimal speed profile for the safe journey of trains will be obtained. For the simulation, real data from rail tracks of Tehran metro have been incorporated. [ABSTRACT FROM AUTHOR]

The design of low voltage grid tariffs can be constrained by several political objectives for the electricity sector, such as energy conservation or an increase of local generation. This leads to trade‐offs between these objectives. In this study, a multi‐objective model is proposed, to address this task in a European context. The model calculates Pareto‐optimal shares of the capacity and energy components for different sets of decision variables. In a case study, the model is implemented for a number of tariff structures such as a flat tariff and a time of use tariff. Results show the trade‐off between the objectives. They also show the impact of rising levels of local generation and of a variation in the tariff structure. These case study results outline the importance to formalise high level political objectives and to include them in the tariff design process. [ABSTRACT FROM AUTHOR]

This note introduces a derivation of the sensitivities of a probability of failure with respect to decision variables. For instance, the gradient of the probability of failure with respect to deterministic design variables might be needed in RBDO. These sensitivities might also be useful for Uncertainty-based Multidisciplinary Design Optimization. The difficulty stems from the dependence of the failure domain on variations of the decision variables. This dependence leads to a derivative of the indicator function in the form of a Dirac distribution in the expression of the sensitivities. Based on an approximation of the Dirac, an estimator of the sensitivities is analytically derived in the case of Crude Monte Carlo first and Subset Simulation. The choice of the Dirac approximation is discussed. [ABSTRACT FROM AUTHOR]

This study presents new robust linear matrix inequality (LMI) conditions for robust ℋ∞ full‐order filter design of discrete‐time linear systems affected by time‐invariant uncertainty and a time‐varying state delay. Thanks to the use of a larger number of slack variables, the proposed robust LMI conditions contain and generalise other results from the literature. LMI relaxations based on homogeneous polynomial matrices of arbitrary degree are used to determine the state‐space realisation of the full‐order filter, that can also be implemented with delayed state terms whenever the time‐delay is available in real time. As another contribution, an iterative LMI‐based procedure involving the decision variables is proposed to improve the ℋ∞ filter performance. Numerical experiments illustrate the better performance of the proposed filter when compared to other approaches available in the literature. [ABSTRACT FROM AUTHOR]

In this paper a method is proposed to solve fractional programming problem in which the functional relationship between the decision variables and the objective function is not completely known. Here we established the crisp functional relationship between the objective function and the decision variables and solve the resulting programming problem to find a fair optimal solution to the original problem. An example is given in the last to illustrate the result. [ABSTRACT FROM AUTHOR]

Copyright of Revista EIA is the property of Escuela de Ingenieria de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Integrating logical constraints into optimal control problems is not an easy task. In fact, optimal control problems are usually continuous while logical constraints are naturally expressed by integer (binary) variables. In this article we are interested is a particular form of an LQR optimal control problem: the energy (controlL2 norm) is to be minimized, system dynamic is linear and logical constraints on the control use are to be fulfilled. Even if the starting continuous problem is not a complicated one, difficulties arise when integrating the additional logical constraints. First, we will present two different ways of modeling the problem, both of them leading us to Mixed Integer Problems. Furthermore, algorithms (Generalized Outer Approximation, Benders Decomposition and Branch and Cut) are applied on each model and results analyzed. We also present a Benders Decomposition algorithm variant that is adapted to our problem (taking into account its particular form) and we will conclude by looking at the optimal solutions obtained in an interesting physical example: the harmonic spring. [ABSTRACT FROM AUTHOR]

Noncoherent diversity reception of M-ary frequency-shift keying (MFSK) signals becomes increasingly important due to its widespread use in wireless communications. Its error performance analysis, however, is not available in the literature except for the simple case using binary modulation. We address the problem directly based on the decision variables, ending up with closed-form solutions to both conditional and average error probabilities for a general noncoherent MFSK diversity system operating on Rayleigh, Rician, and Nakagami fading channels. The solutions involve some higher order derivatives, and efficient recursive algorithms have been derived for their calculation. The solutions are very general permitting arbitrary diversity order, symbol size, channel parameters, and antenna-array covariance matrix. Numerical examples are also presented for illustration [ABSTRACT FROM PUBLISHER]

The genetic algorithm (GA) and simulated annealing algorithm (SA) are empirically compared for the problem of optimizing the topological design of a network. In addition to the usual problem of optimizing only the placement of links, in this paper the number and placement of concentrators are also decision variables for a class of problems using a real set of concentrators, links, and traffic. The average GA solution cost less than the average SA solution [ABSTRACT FROM PUBLISHER]

This study presents an attempt to discover the effect of sample size on the financial outcome derived by stand-level optimization with individual tree modeling. The initial stand structure was altered to reflect sparse, average, and dense Scots pine (Pinus sylvestris L.) stands. The stands had varying numbers of stems but identical weighted median diameters and stand basal areas. The hypothetical Weibull diameter distributions were solved according to the parameter recovery method. The trees were systematically sampled with respect to the tree basal area corresponding to sample sizes of 10, 20, or 40 trees. We optimized the stand management with varying numbers of sample trees and with varying stand structures and compared the optimal solutions with respect to the objective function value (maximum net present value) and underlying management schedule. The results for the pine stands in southern and central Finland indicated that the variations in the objective function value relating to sample size were minor (<2.6%) in the sparse and average stand densities but exceeded 3% in the dense stands. Generally, the stand density is not always known, and thus, we may need to generalize the average density for all cases in question. This assumption, however, resulted in overestimations with respect to the optimal rotation period and financial performance in this study. The overestimations in the net present value decreased along with the increasing sample size, from 22% to 14% in the sample sizes of 10 and 40 trees, respectively. [ABSTRACT FROM AUTHOR]