scholarly journals Multidimensional Aspects of Sustainable Biofuel Feedstock Production

2021 ◽  
Vol 13 (3) ◽  
pp. 1424
Author(s):  
Anna Raschke ◽  
J. Sebastian Hernandez-Suarez ◽  
A. Pouyan Nejadhashemi ◽  
Kalyanmoy Deb

Bioenergy is becoming increasingly relevant as an alternative to fossil fuels. Various bioenergy feedstocks are suggested as environmentally friendly solutions due to their positive impact on stream health and ability to sequester carbon, but most evaluations for bioenergy feedstocks have not evaluated the implications of bioenergy crop production holistically to date. Through the application of multi-objective optimization on 10 bioenergy feedstock rotations in a Michigan watershed, a Pareto front is searched to identify optimal trade-off solutions for three objective functions representing stream health, environmental emissions/carbon footprint, and economic feasibility. Various multi-criteria decision-making techniques are then applied to the resulting Pareto front to select a set of most-preferred trade-off solutions, which are compared to optimal solutions from each individual objective function. The most-preferred trade-off solutions indicate that a diverse mix of rotations are necessary to optimize all three objectives, whereas the individually optimal solutions do not consider a diverse range of feedstocks, thereby making the proposed multi-objective treatment an important and pragmatic strategy.

Author(s):  
Rui Zhu ◽  
Puyu Cao ◽  
Yang Wang ◽  
Chao Ning

Abstract Flow distortions occur at the outlet section of the intake duct owing to its shape properties, which is a component of water-jet propulsion. Since the noticeable influence of intake’s flow characteristics upon propulsive efficiency, it’s necessary to focus on intake duct redesign. In this paper, a systematic methodology for reducing flow distortions and power losses within the intake duct through a shape optimization process was obtained. In addition, the mechanism of flow distortions was also developed. The flush type inlet applied in the marine vessel with the speed of 30 knots was chosen as research project. Four characteristic parameters were set as optimization variables depending on the geometrical relationship of thirteen characteristic parameters referred to the duct longitudinal midsection, which were the ramp angle α, the radius of the upper lip R3, the radius of the lower lip R4 and the lip height h respectively. Subsequently, a sample space was built by Latin Hypercube Sampling (LHS) and the parameters were normalized in the range of 0 to 1. With the commercial software CFX, the numerical simulation was accomplished driven by SST k-ω turbulence model. Multi-objective optimization based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized to minimize the non-uniformity at outlet section and maximize the minimal pressure at lip simultaneously. Moreover, the Radial Basis Function (RBF) neural network was employed to approximate the functional relationship between variables and objectives, which could be applied in the NSGA-II to get the Pareto Front. The minimum non-uniformity point and the trade-off point (The point both satisfies the minimum non-uniformity and the maximum minimal pressure at lip strategically) were selected from the Pareto Front. With regard to the characteristic parameters of the trade-off point, the ramp angle, the radius of the upper lip, the radius of the lower lip and the lip height are 31.91°, 11.42 mm, 400.97 mm and 55.43 mm respectively. Meanwhile, the characteristic parameters of the minimum non-uniformity point are 30.22°, 25.59 mm, 166.65 mm and 89.90 mm respectively. Ultimately, the duct outflow characteristics of prototype and optimization are compared. In terms of the trade-off point, the minimal pressure at lip increases 66.40% to −24488.93 Pa and the non-uniformity has a drop of 4.56% to 0.1571. The non-uniformity of the minimum point is 0.1481 which is reduced by 10.02%. Through the optimization of duct shape, the secondary flow (Dean vortices) is suppressed effectively. This paper is expected to provide a better comprehension of the flow field within the intake duct of water-jet propulsion.


Author(s):  
Fakhre Ali ◽  
Konstantinos Tzanidakis ◽  
Ioannis Goulos ◽  
Vassilios Pachidis ◽  
Roberto d'Ippolito

A computationally efficient and cost effective simulation framework has been implemented to perform design space exploration and multi-objective optimization for a conceptual regenerative rotorcraft powerplant configuration at mission level. The proposed framework is developed by coupling a comprehensive rotorcraft mission analysis code with a design space exploration and optimization package. The overall approach is deployed to design and optimize the powerplant of a reference twin-engine light rotorcraft, modeled after the Bo105 helicopter, manufactured by Airbus Helicopters. Initially, a sensitivity analysis of the regenerative engine is carried out to quantify the relationship between the engine thermodynamic cycle design parameters, engine weight, and overall mission fuel economy. Second, through the execution of a multi-objective optimization strategy, a Pareto front surface is constructed, quantifying the optimum trade-off between the fuel economy offered by a regenerative engine and its associated weight penalty. The optimum sets of cycle design parameters obtained from the structured Pareto front suggest that the employed heat effectiveness is the key design parameter affecting the engine weight and fuel efficiency. Furthermore, through quantification of the benefits suggested by the acquired Pareto front, it is shown that the fuel economy offered by the simple cycle rotorcraft engine can be substantially improved with the implementation of regeneration technology, without degrading the payload-range capability and airworthiness (one-engine-inoperative) of the rotorcraft.


Author(s):  
Tipwimol Sooktip ◽  
Naruemon Wattanapongsakorn

In multi-objective optimization problem, a set of optimal solutions is obtained from an optimization algorithm. There are many trade-off optimal solutions. However, in practice, a decision maker or user only needs one or very few solutions for implementation. Moreover, these solutions are difficult to determine from a set of optimal solutions of complex system. Therefore, a trade-off method for multi-objective optimization is proposed for identifying the preferred solutions according to the decision maker’s preference. The preference is expressed by using the trade-off between any two objectives where the decision maker is willing to worsen in one objective value in order to gain improvement in the other objective value. The trade-off method is demonstrated by using well-known two-objective and three-objective benchmark problems. Furthermore, a system design problem with component allocation is also considered to illustrate the applicability of the proposed method. The results show that the trade-off method can be applied for solving practical problems to identify the final solution(s) and easy to use even when the decision maker lacks some knowledge or not an expert in the problem solving. The decision maker only gives his/her preference information.  Then, the corresponding optimal solutions will be obtained, accordingly.


2019 ◽  
Vol 19 (8) ◽  
pp. 2338-2346
Author(s):  
Silvia Carpitella ◽  
Bruno Brentan ◽  
Idel Montalvo ◽  
Joaquín Izquierdo ◽  
Antonella Certa

Abstract This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the non-dominated sorting genetic algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria.


Author(s):  
Y. Haralampidis ◽  
M. Pavlidou ◽  
C. Papadimitriou

The structural identification problem is formulated in a multi-objective context that allows the simultaneous minimization of the various objectives related to the fit between measured and model predicted data. Thus, the need for using arbitrary weighting factors for weighting the relative importance of each objective is eliminated. The set of admissible solutions are known in multi-objective optimization terminology as Pareto optimal solutions and constitute acceptable compromise solutions that cannot be improved in any objective without causing degradation in one other objective. The strength Pareto evolutionary algorithm is used to obtain the set of Pareto solutions. The use of the proposed methodology is illustrated by identifying the Pareto front and the corresponding set of optimal solutions for a model of a scaled laboratory structure using experimentally obtained modal data.


2021 ◽  
Vol 3 ◽  
Author(s):  
Viviane De Buck ◽  
Philippe Nimmegeers ◽  
Ihab Hashem ◽  
Carlos André Muñoz López ◽  
Jan Van Impe

The highly competitive nature of the chemical industry requires the optimisation of the design and exploitation of (bio-)chemical processes with respect to multiple, often conflicting objectives. Genetic algorithms are widely used in the context of multi-objective optimisation due to their overall straightforward implementation and numerous other advantages. NSGA-II, one of the current state-of-the-art algorithms in genetic multi-objective optimisation has, however, two major shortcomings, inherent to evolutionary algorithms: 1) the inability to distinguish between solutions based on their mutual trade-off and distribution; 2) a problem-irrelevant stopping criterion based on a maximum number of iterations. The former results in a Pareto front that contains redundant solutions. The latter results in an unnecessary high computation time. In this manuscript, a novel strategy is presented to overcome these shortcomings: t-domination. t-domination uses the concept of regions of practically insignificant trade-off (PIT-regions) to distinguish between solutions based on their trade-off. Two solutions that are located in each other’s PIT-regions are deemed insignificantly different and therefore one can be discarded. Additionally, extrapolating the concept of t-domination to two subsequent solution populations results in a problem-relevant stopping criterion. The novel algorithm is capable of generating a Pareto front with a trade-off-based solution resolution and displays a significant reduction in computation time in comparison to the original NSGA-II algorithm. The algorithm is illustrated on benchmark scalar case studies and a fed-batch reactor case study.


2021 ◽  
Author(s):  
MD ZOHEB HASSAN

<div>Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the $\epsilon$-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes. </div>


Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.


2019 ◽  
Vol 6 (04) ◽  
Author(s):  
ASHUTOSH UPADHYAYA

A study was undertaken in Bhagwanpur distributary of Vaishali Branch Canal in Gandak Canal Command Area, Bihar to optimally allocate land area under different crops (rice and maize in kharif, wheat, lentil, potato in rabi and green gram in summer) in such a manner that maximizes net return, maximizes crop production and minimizes labour requirement employing simplex linear programming method and Multi-Objective Fuzzy Linear Programming (MOFLP) method. Maximum net return, maximum agricultural production, and minimum labour required under defined constraints (including 10% affinity level of farmers to rice and wheat crops) as obtained employing Simplex method were ` 3.7 × 108, 5.06 × 107 Kg and 66,092 man-days, respectively, whereas Multi-Objective Fuzzy Linear Programming (MOFLP) method yielded compromised solution with net return, crop production and labour required as ` 2.4 × 108, 3.3 × 107Kg and 1,79,313 man-days, respectively. As the affinity level of farmers to rice and wheat crops increased from 10% to 40%, maximum net return and maximum production as obtained from simplex linear programming method and MOFLP followed a decreasing trend and minimum labour required followed an increasing trend. MOFLP may be considered as one of the best capable ways of providing a compromised solution, which can fulfill all the objectives at a time.


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