Comparison of co-current and counter-current flow in a bifunctional reactor containing ammonia synthesis and 2-butanol dehydrogenation to MEK

In this study, a multi-tubular thermally coupled packed bed reactor in which simultaneous production of ammonia and methyl ethyl ketone (MEK) takes place is simulated. The simulation results are presented in two co-current and counter-current flow modes. Based on this new configuration, the released heat from the ammonia synthesis reaction as an extremely exothermic reaction in the inner tube is employed to supply the required heat for the endothermic 2-butanol dehydrogenation reaction in the outer tube. On the other hand, MEK and hydrogen are produced by the dehydrogenation reaction of 2-butanol in the endothermic side, and the produced hydrogen is used to supply a part of the ammonia synthesis feed in the exothermic side. Thus, 30.72% and 31.88% of the required hydrogen for the ammonia synthesis are provided by the dehydrogenation reaction in the co-current and counter-current configurations, respectively. Also, according to the thermal coupling, the required cooler and furnace for the ammonia synthesis and 2-butanol dehydrogenation conventional plants are eliminated, respectively. As a result, operational costs, energy consumption and furnace emissions are considerably decreased. Finally, a sensitivity analysis and optimization are applied to study the effect of the main process parameters variation on the system performance and obtain the minimum hydrogen make-up flow rate, respectively.     

An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Interactive design exploration for constrained meshes

In architectural design, surface shapes are commonly subject to geometric constraints imposed by material, fabrication or assembly. Rationalization algorithms can convert a freeform design into a form feasible for production, but often require design modifications that might not comply with the design intent. In addition, they only offer limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm.We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimization subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a unified way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved efficiently and accurately.Based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system offers full control over the exploration process, by providing direct access to the specification of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

An agile model for the eco-design of electric vehicle Li-ion batteries

Numerous design choices need to be made at several levels when designing high-tech products: technology, processes, architecture, components, materials… and these choices need to be made in relation to the product life cycle with the corresponding experts for each stage of the life cycle. At the same time, to ensure product sustainability, a specific focus on the future potential environmental impacts is highly recommended. In this research, an agile model is proposed to help designers make decisions while monitoring environmental performance indicators of high-tech solutions. The concept of Critical Product Life Cycle Parameters had to be introduced to facilitate the eco-design of the final product. The approach is illustrated by the Electric Vehicle Li-Ion Batteries case study.     

Biologically Inspired Design of Context-Aware Smart Products

The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware.     

Ions motion algorithm for solving optimization problems

This paper proposes a novel optimization algorithm inspired by the ions motion in nature. In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization. The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance. The performance of this algorithm is benchmarked on 10 standard test functions and compared to four well-known algorithms in the literature. The results demonstrate that the proposed algorithm is able to show very competitive results and has merits in solving challenging optimization problems.     

A design of experiments investigation into dry separation using a Knelson Concentrator

Enhanced gravity, or centrifugal, separators have revolutionised gold processing over the past decades, significantly increasing the recovery of fine (−100 μm) free gold. One of the main drawbacks of centrifugal gravity concentrators is the large volume of water required (even if it is all recycled). With water becoming an ever increasingly important “commodity”, reducing this is of importance both from an environmental and a monetary point of view. This work investigated operating a laboratory scale Knelson Concentrator with a dry feed and using air as the fluidising medium. The feed used was a synthetic mixture of tungsten and quartz, used to mimic a gold ore. The response surface method and central composite design techniques were used to design the experiments and to model the results, with the experimental variables being the bowl speed (G-Level), air fluidising pressure and the feed rate. The models corresponded well to the experimental results, indicating that for this experimental setup, the optimal conditions were a bowl G-Level of 40 G, a feed rate of 220 g/min and an air fluidising pressure of 8 psi.