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.     

Implementing land-use and ecosystem service effects into an integrated bioenergy value chain optimisation framework

This study presents a multi-objective optimisation model that is configured to account for a range of interrelated or conflicting questions with regard to the introduction of bioenergy systems. A spatial-temporal mixed integer linear programming model ETI-BVCM (Energy Technologies Institute – Bioenergy Value Chain Model) (ETI, 2015b, Newton-Cross, 2015, Samsatli et al., 2015) was adopted and extended to incorporate resource-competing systems and effects on ecosystem services brought about by the land-use transitions in response to increasing bioenergy penetration over five decades. The extended model functionality allows exploration of the effects of constraining ecosystem services impacts on other system-wide performance measures such as cost or greenhouse gas emissions. The users can therefore constrain the overall model by metric indicators which quantify the changes of ecosystem services due to land use transitions. The model provides a decision-making tool for optimal design of bioenergy value chains supporting an economically and land-use efficient and environmentally sustainable UK energy system while still delivering multiple ecosystem services.     

Pyrolysis kinetics and multi-objective inverse modelling of cellulose at the microscale

The chemistry of pyrolysis, together with heat transfer, drives ignition and flame spread of biomass materials under many fire conditions, but it is poorly understood. Cellulose is the main component of biomass and is often taken as a surrogate for biomass. Its chemistry of pyrolysis is simpler and dominates the pyrolysis of biomass. Many reaction schemes with corresponding kinetic parameters can be found in the literature for the pyrolysis of cellulose, but their appropriateness for fire is unknown. This study investigated inverse modelling and blind predictions of six reaction schemes of different complexities for isothermal and non-isothermal thermogravimetric experiments. We used multi-objective optimisation to simultaneously and separately inverse model the kinetic parameters of each reaction scheme to several experiments. Afterwards we tested these parameters with blind predictions. For the first time, we reveal a set of equally viable solutions for the modelling of pyrolysis chemistry of different experiments. This set of solutions is called a Pareto front, and represents a trade-off between predictions of different experiments. It stems from the uncertainty in the experiments and in the modelling. Parameters derived from non-isothermal experiments compared well with the literature, and performed well in blind predictions of both isothermal and non-isothermal experiments. Complexity beyond the Broido-Shafizadeh scheme with seven parameters proved to be unnecessary to predict the mass loss of cellulose; hence, simple reaction schemes are most appropriate for macroscale fire models. Modellers should, therefore, use simple reaction schemes to model pyrolysis in macroscale fire models.     

A review and critique on integrated production–distribution planning models and techniques

Optimisation modelling of integrated production–distribution (P–D) plans has raised significant interest among both researchers and practitioners over the past two decades. This paper provides the readers with a comprehensive review and critique on the current P–D planning and optimisation literature. We classify the published P–D planning models into seven categories based on their degree of complexity and hence capability in addressing real-life scenarios. Summary tables highlight the main characteristics of the selected models at each category. Next, the paper reclassifies and evaluates the proposed models based on the solution techniques used. Lastly, the unaddressed areas in the current literature are highlighted, important managerial implications are proposed and directions for future research in the area are suggested.     

Optimisation model for integration of cooling and heating systems in large industrial plants

Large industrial plants have often hundreds of heating and cooling heat exchangers. A common situation is that cooling demands of the processes are satisfied without any deeper analysis of the overall impact of the cooling systems on the plant’s economy or the environment. If cooling water is available it is used as much as needed and then pumped back to the river, some degrees warmer.An optimisation model was developed for integration of cooling and heating systems to tackle the problem. An industrial cooling system is a complex energy system comprising different options of producing cooling, distribution pipelines for cold media and cooling storages. Integration of power generation and heating systems to the cooling systems was included in the model. An illustrative example is presented in the paper. 10 process streams with cooling demand and 10 streams with heating demand were chosen, situated at different locations at the plant site. The optimal matches between the streams were found together with the sizes of the heat exchangers and the demands of hot and cold utilities. The costs of pipelines and the pumping costs of the streams are included in the model. The model can be used in the design of greenfield and retrofit investments and in versatile what-if analyses of the plant design or operation.     

Travel Time Reliability in Vehicle Routing and Scheduling with Time Windows

This paper presents calibration of the Vehicle Routing and scheduling Problems with Time Windows-Probabilistic (VRPTW-P) model which takes into account the uncertainty of travel times. Probe vehicle data of travel times were obtained from usual operation of pickup-delivery trucks in South Osaka area. The optimal solution of the VRPTW-P model resulted in reducing total cost, running times and CO₂, NOx and Particle Materials (PM) emissions compared with the usual operation. This is attributed to better routing of VRPTW-P to choose more reliable roads. Therefore, VRPTW-P can contribute to establish efficient and environmentally friendly delivery systems in urban area.     

A method for assessing the effect of water quality changes on plumbosolvency using random daytime sampling

The Mann-Whitney U-test is used to demonstrate the impact of phosphate on lead concentrations measured at customer properties. This test is statistically robust and particularly efficient for the type of distributions encountered in lead random daytime sampling. This non-parametric technique is developed to provide a best estimate of the lead reduction that results from a change in plumbosolvency conditions. The method is illustrated with compliance data collected before and after the introduction of phosphate at customer properties in the north west of England. Limitations due to operational factors are highlighted. Using a Monte Carlo simulation of the variability of lead random daytime samples it is shown that the method might be practical when assessing the impact of incremental changes in phosphate concentration on plumbosolvency.     

Simultaneous recurrent neural network trained with non-recurrent backpropagation algorithm for static optimisation

This paper explores feasibility of employing the non-recurrent backpropagation training algorithm for a recurrent neural network, Simultaneous Recurrent Neural network, for static optimisation. A simplifying observation that maps the recurrent network dynamics, which is configured to operate in relaxation mode as a static optimizer, to feedforward network dynamics is leveraged to facilitate application of a non-recurrent training algorithm such as the standard backpropagation and its variants. A simulation study that aims to assess feasibility, optimizing potential, and computational efficiency of training the Simultaneous Recurrent Neural network with non-recurrent backpropagation is conducted. A comparative computational complexity analysis between the Simultaneous Recurrent Neural network trained with non-recurrent backpropagation algorithm and the same network trained with the recurrent backpropagation algorithm is performed. Simulation results demonstrate that it is feasible to apply the non-recurrent backpropagation to train the Simultaneous Recurrent Neural network. The optimality and computational complexity analysis fails to demonstrate any advantage on behalf of the non-recurrent backpropagation versus the recurrent backpropagation for the optimisation problem considered. However, considerable future potential that is yet to be explored exists given that computationally efficient versions of the backpropagation training algorithm, namely quasi-Newton and conjugate gradient descent among others, are also applicable for the neural network proposed for static optimisation in this paper.     

Optimisation of processing variables in the preparation of sweet potato chips using response surface methodology

Models capable of predicting the product quality of sweet potato chips have been developed using response surface methodology and used to determine the optimum processing conditions. Moisture loss, oil uptake, crispness (measured using a bending-snapping test in the TA.XT2 Texture Analyser), and sensory attributes such as colour, flavour, and texture were used to assess the product quality in the preparation of sweet potato chips. The optimum conditions which were attained for maximum moisture loss (11.65% on wet basis), minimum oil uptake (2.57%), crispness (794.37 g), colour score (7), flavour score (7) and texture score (7) were: frying temperature, 174.7 °C; salt concentration, 0.45%; citric acid concentration, 0.37%; potassium metabisulphite concentration, 0.65%; and frying time, 26 s.     

Optimization of evaporative fluid coolersOptimisation des refroidisseurs de liquide évaporatifs

In the paper the optimization of geometrical and operating parameters for evaporative fluid coolers has been presented. The algorithm of optimizational calculations contains:

• the mathematical model of heat and mass transfer in evaporative fluid coolers;

• model of operating costs based, among others, on the Zalewski–Gryglaszewski relations developed by the authors for evaluation of air pressure drops in the heat exchanger;

• model of evaporative heat exchangers production costs.

Optimization problem formulated for non-linear objective functions with inequality and equality constraints has been solved using Schittkowski's method based on quadratic programming. Two optimization problems have been considered. A solution to the first problem consists in the design of a heat exchanger with such geometrical parameters which ensure its maximum heat capacity at minimal total costs. A result to the second optimization problem are such thermal and flow parameters which will ensure minimum operating costs.The results of optimization calculations presented in the paper are accompanied by their experimental verification.