Extraction optimisation,antioxidant activity and inhibition on α-amylase and pancreatic lipase of polyphenols from the seeds of Syzygium cumini

The objectives of this study were to determine the optimal extraction conditions of polyphenols from Syzygium cumini seeds by response surface methodology and investigate their antioxidant activity and inhibition on α-amylase and pancreatic lipase. As results, the optimal extraction conditions in the ultrasonic extraction process which maximised total polyphenols content, minimised the IC₅₀ values of α-amylase and pancreatic lipase were determined as follows: extraction time 60 min, ethanol concentration 63% and solvent/solid ratio 44 mL g⁻¹. The main phenolic compounds in partially purified fraction of Syzygium cumini seeds were catechin, epicatechin, kaempferol, gallic, 5-caffeoylquinic, caffeic and ferulic acids. In addition, the partially purified fraction inhibited 87.66 ± 5.55 and 86.61 ± 3.15% of α-amylase and pancreatic lipase, respectively. The results suggested that Syzygium cumini seeds could be explored as a natural antioxidant and could be used as a source of highly antidiabetic and anti-obesity bioactive compounds.     

A P-graph approach for the synthesis of national-wide bio-hydrogen network from palm oil mill effluent

The shift from conventional hydrogen production that utilised fossil-based energy towards a more sustainable practice is essential. Recently, the interest in utilising renewable hydrogen sources associated with bio-gas has risen. This work aims to develop a P-graph methodology for bio-hydrogen network synthesis, where oil refineries and ammonia plants act as bio-hydrogen sinks, while palm oil mills serve as the bio-hydrogen sources. The latter produces huge amount of palm oil mill effluent (POME) which may be converted to bio-hydrogen. The model is optimised with the aim to minimise the total network cost, whilst fulfilling the demand of all hydrogen sinks. Two case studies in Malaysia are used to illustrate the effectiveness of the proposed model. Results show that the overall network cost can be reduced by 45.6% (first case study) and 85.8% (second case study) when bio-hydrogen is supplied from the abundant POME in the study areas. Besides, the capability of the proposed P-graph framework in conducting uncertainty analysis and technology benchmarking study, are also demonstrated.     

Optimum stiffened panel design with fundamental frequency constraint

A generic analysis/design method for prismatic plate assemblies, using exact plate stiffnesses which are transcendental functions of loading and/or vibration frequency, has long been validated by the established program . Hitherto vibrations were permitted only in analysis because design uses the Wittrick–Williams algorithm to modify the design predicted by a linear optimiser to be ‘just stable’ by scaling all layer thicknesses by F. This works because buckling loads, unlike the fundamental frequency f₁, vary monotonically with F. The paper considers additionally scaling all plate breadths by F^α such that the buckling loads and f₁ all vary monotonically with F. Theoretical consideration of simple limiting cases establishes bounds on the permitted values of α as 0.1≤α≤0.4. Then results for typical stiffened panels demonstrate the method to be correct and are used to investigate the value of α which is most effective in returning stabilised designs of least mass and with least computation.     

自身形状优化原则:薄壁型材截面承载力的优化

为实现经济效益,已对大批量生产的钢结构产品的优化进行了广泛研究。旨在不削弱结构构件的强度和实用性的条件下,尽量减少材料的用量。当前研究主要集中于传统截面形状主要尺寸的优化,而并未考虑采用新的最佳截面。介绍了一种新的优化方法,利用遗传算法(GA)的进化和自适应特点,使截面的自身形状达到最佳效果。采用该方法对薄壁型材的截面承载力进行优化,验证了该方法的可行性和精确性。特别地,通过简单参数对截面的优化方法中,这种分析方法是众所周知的,即双轴对称闭口截面的优化。结果表明,经过几代演化,截面自身形状产生出精确的最优解。给出了影响收敛的因素。该方法可推广至冷弯型钢开口截面柱的优化。     

Numerical simulation and design optimisation of an integrally-heated tool for composite manufacturing

Tooling design is crucial for the production of cost-effective and durable composite products. As part of the current search for cost reduction, integrally-heated tooling is one of the technologies available for ‘out-of-autoclave’ processing of advanced thermoset polymer composites. Despite their advantages, integrally-heated tools can suffer from uneven distribution of temperature, variability in heat flow rate and inconsistency in heating/cooling time. This research, therefore, investigates a number of design variables such as shape and layout of heating channels in order to improve the thermal performance of an integrally-heated tool. Design of Experiments (DoE) has been carried out using Taguchi’s Orthogonal Array (OA) method to set several combinations of design parameters. Each of these design combinations has been evaluated through numerical simulation to investigate heating time and mould surface temperature variation. The simulation results suggest that the layout of the channels and their separation play a vital role in the thermal performance. Signal-to-Noise (S/N) ratio and analysis of variance (ANOVA) have been applied to the results obtained to identify the optimal design combination of the integrally-heated tool. Statistical analysis reveals that the heating performance of an integrally-heated tool can be significantly improved when the channels’ layout is parallel. The shape of the channels has negligible effect and the distance between the channels should be determined based on the production requirement.     

Optimisation of metallurgical performance of industrial flotation column using neural network and gravitational search algorithm

Abstract

Extensive advances in computational techniques allowed researchers to develop new search strategies to be used in function optimisation problems. In the present study, an optimisation algorithm, which is called gravitational search algorithm (GSA), and neural networks are integrated to optimise the metallurgical performance of an industrial flotation column at a copper concentrator. In this optimisation algorithm, a collection of masses interact with each other based on the Newtonian gravity and the laws of motion. Accordingly, an industrial flotation column is operated at different operating conditions (i.e. gas velocity, slurry solids %, frother dosage and froth depth) and its metallurgical efficiency is modelled using neural networks. The developed neural network models are used as objective functions for optimisation by GSA. The obtained results indicate that industrial operations can be successfully modelled and optimiszed by applying intelligent techniques.

L’avancement extensif des techniques informatiques a permis aux chercheurs de développer de nouvelles stratégies de recherche à utiliser dans les problèmes d’optimisation de fonction. Dans cette étude, on intègre un algorithme d’optimisation, qui s’appelle Algorithme de Recherche Gravitationnelle (GSA), et des réseaux neuronaux pour l’optimisation du rendement métallurgique d’une colonne industrielle de flottation, à un concentrateur de cuivre. Dans cet algorithme d’optimisation, une collection de masses interagissent, en se basant sur la gravité newtonienne et sur les lois du mouvement. Ainsi, on opère une colonne industrielle de flottation avec différentes conditions de fonctionnement (c’est-à-dire vitesse du gaz, % en solides de la suspension, dose de l’agent moussant et profondeur de l’écume) et l’on modélise son efficacité métallurgique en utilisant les réseaux neuronaux. On utilise les modèles développés du réseau neuronal comme fonctions objectives pour l’optimisation par le GSA. Les résultats obtenus indiquent que l’on peut modéliser avec succès et optimiser les opérations industrielles en appliquant des techniques intelligentes.     

Determination of optimal location to monitor temperature in low pressure die casting process

Abstract

Controlling and eliminating defects, such as macroporosity, in castings is a continuing challenge that manufacturers must continually address. Since the encapsulation of liquid regions by a solid shell and subsequent formation of macroporosity cannot be detected during casting, the die temperature, which is routinely measured, has been used as an indirect indicator of this defect. A finite element model has been developed to predict the evolution of temperature as well as the volume of encapsulated liquid in a casting with a high propensity to form macroporosity. The boundary conditions in the model were iteratively adjusted until the temperature predictions matched the experimental data for a variety of operational conditions. A model based methodology has been developed to analyse the correlation between the die temperature and the encapsulated liquid volume. This methodology is employed to assess the suitability of different in-cycle die temperatures for use as indicators of macroporosity formation, and to help determine the optimal location to monitor temperature for the purpose of minimising macroporosity.     

Benefits of oxygen enrichment in sponge iron rotary kiln emerging from data-driven modelling

Optimal oxygen enrichment conditions for sponge iron rotary kiln have been successfully explored on an industrial scale using a data-driven model. A multi-objective optimisation by genetic algorithm (MOGA) is employed to find the favourable conditions. The objective function for MOGA is derived from neural networks using pre-processed operational data. From industrial experimentations guided by the optimum conditions predicted by the present model, it emerged that when the coal fines injection is maintained at 1.75?tph and the oxygen enrichment is 8 Nm³?t^?1 of sponge iron, a reduction in the specific air requirement from 2609 to 2150?Nm³?t^?1 was obtained, while the end-zone bed temperature remained under control at 1132°C. These conditions resulted in a reduction of specific coal consumption by 6%, an enhancement in the sponge iron production by 6% and an increase in the rotary kiln campaign life from 50 to 100 days.     

Optimisation as a process for understanding and managing river ecosystems

Optimisation can assist in the management of riverine ecosystems through the exploration of multiple alternative management strategies, and the evaluation of trade-offs between conflicting objectives. In addition, it can facilitate communication and learning about the system. However, the effectiveness of optimisation in aiding decision making for ecological management is currently limited by four major challenges: identification and quantification of ecosystem objectives; representation of ecosystems in predictive simulation models; specification of objectives and management alternatives in an optimisation framework; and evaluation of model results against actual ecological outcomes. This study evaluates previous literature in ecology, optimisation and decision science, and provides a strategy for addressing the challenges identified. It highlights the need for better recognition and analysis of assumptions in optimisation modelling as part of a process that generates and shares knowledge.     

Desirability based multiobjective optimisation of abrasive wear and frictional behaviour of aluminium (Al/3.25Cu/8.5Si)/fly ash composites

Aluminium alloy (Al/3.25Cu/8.5Si) composites reinforced with fly ash particles of three different size ranges (53–75?μm, 75–103?μm and 103–125?μm) in 3, 6 and 9 wt-% were fabricated using liquid metallurgy technique. Pin on disc abrasive wear tests were carried against the disc surface fixed with SiC emery paper (120 grades). A mathematical model was developed to predict the abrasive wear and coefficient of friction of the composites. Analysis of variance technique was used to check the validity of the developed model. Composites reinforced with coarse fly ash particles exhibited better abrasive wear resistance than those reinforced with fine fly ash particles. Abrasive wear in composites with fine fly ash particles is a combination of adhesive wear and abrasive wear. Larger fly ash particles present in composites gets fractured into fine particles and entrapped between the composite pin and the disc, thereby decreasing the wear rate. Worn surfaces of the pins were then analysed using scanning electron microscopy to study the wear mechanisms of the composites. The abrasive wear was optimised using desirability based multiobjective optimisation technique.