Multi-objective optimisation of the cure of thick components

This paper addresses the multi-objective optimisation of the cure stage of composites manufacture. The optimisation aims to minimise the cure process duration and maximum temperature overshoot within the curing part by selecting an appropriate thermal profile. The methodology developed combines a finite element solution of the heat transfer problem with a Genetic Algorithm. The optimisation algorithm approximates successfully and consistently the Pareto optimal front of the multi-objective problem in a variety of characteristic geometries of varying thickness. The results highlight the efficiency opportunities available in comparison with standard industrial cure profiles. In the case of ultra-thick components improvements of up to 70% in terms of overshoot and 14 h in terms of process time, compared to conventional cure profiles for ultra-thick components, can be achieved. In the case of thick components reduction up to 50% can be achieved in both temperature overshoot and process duration

A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm

The primary objective of this paper is to introduce a new holistic approach to the design of alloy steels based on a biologically inspired multi-objective immune optimisation algorithm. To this aim, a modified population adaptive based immune algorithm (PAIA2) and a multi-stage optimisation procedure are introduced, which facilitate a systematic and integrated fuzzy knowledge extraction process. The extracted (interpretable) fuzzy models are able to fully describe the mechanical properties of the investigated alloy steels. With such knowledge in hand, locating the ‘best’ processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels is possible. The research has also enabled to unravel the power of multi-objective optimisation (MOP) for automating and simplifying the design of the heat treated alloy steels and hence to achieve ‘right-first-time’ production

Optimisation of the VARTM process

This study focuses on the development of a multi-objective optimisation methodology for the vacuum assisted resin transfer moulding composite processing route. Simulations of the cure and filling stages of the process have been implemented and the corresponding heat transfer and flow through porous media problems solved by means of finite element analysis. The simulations involved material sub-models to describe thermal properties, cure kinetics and viscosity evolution. A Genetic algorithm which constitutes the foundation for the development of the optimisation has been adapted, implemented and tested in terms of its effectiveness using four benchmark problems. Two methodologies suitable for multi-objective optimisation of the cure and filling stages have been specified and successfully implemented. In the case of the curing stage the optimisation aims at finding a cure profile minimising both process time and temperature overshoot within the part. In the case of the filling stage the thermal profile during filling, gate locations and initial resin temperature are optimised to minimise filling time and final degree of cure at the end of the filling stage. Investigations of the design landscape for both curing and filling stage have indicated the complex nature of the problems under investigation justifying the choice for using a Genetic algorithm. Application of the two methodologies showed that they are highly efficient in identifying appropriate process designs and significant improvements compared to standard conditions are feasible. In the cure process an overshoot temperature reduction up to 75% in the case of thick component can be achieved whilst for a thin part a 60% reduction in process time can be accomplished. In the filling process a 42% filling time reduction and 14% reduction of degree of cure at the end of the filling can be achieved using the optimisation methodology. Stability analysis of the set of solutions for the curing stage has shown that different degrees of robustness are present among the individuals in the Pareto front. The optimisation methodology has also been integrated with an existing cost model that allowed consideration of process cost in the optimisation of the cure stage. The optimisation resulted in process designs that involve 500 € reduction in process cost. An inverse scheme has been developed based on the optimisation methodology aiming at combining simulation and monitoring of the filling stage for the identification of on-line permeability during an infusion. The methodology was tested using artificial data and it was demonstrated that the methodology is able to handle levels of noise from the measurements up to 5 s per sensor without affecting the quality of the outcome

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application : This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.</p

Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Part of this work has been developed from results obtained during the H2020 “Optimised Energy Efficient Design Platform for Refurbishment at District Level” (OptEEmAL) project, Grant No. 680676

Optimization of multi-holes drilling path using particle swarm optimization

Multi-hole drilling is a manufacturing process that is commonly used in industries. In this process, the tool movement and switching, on average, take 70% of the total machining time. There are many applications of multi-hole drilling, such as in mould, die-making and printed circuit board (PCB). One way to improve the multi-hole drilling is by optimising the tool path in the process. This research aims to model and optimise multi-hole drilling problems using Particle Swarm Optimisation (PSO) algorithm. The study begins by modelling the multi-hole drilling problems using the Travelling Salesman Problem (TSP) concept. The objective function was set to minimise the total tool path distance. Then, the PSO was formulated to minimise total length in multi-hole drilling. The main issue in this stage was to convert the continuous encoding in PSO to permutation problems as in multi-hole drilling. For this purpose, a topological sorting procedure based on the most prominent particle rule was implemented. The algorithm was tested on 15 test problems where between 10 to 150 holes were randomly generated. The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). Then, a validation experiment was conducted by implementing the PSO generated tool path against the commercial CAD-CAM path. In this stage, the machining time was measured. The results from the computational experiment indicated that the proposed PSO algorithm came out with the best solution in 10 out of the 15 test problems. In the meantime, the validation experiment result proved that the PSO generated tool path provides faster machining time compared with the commercial CAD-CAM path by 5% on average. The results clearly showed that PSO has a great potential to be applied in the multi-hole drilling process. The findings from this research could benefit the manufacturing industry to improve their productivity using existing resources

Economic removal of chlorophenol from wastewater using multi-stage spiral-wound reverse osmosis process: simulation and optimisation

YesThe successful use of Reverse Osmosis (RO) process has increased significantly in water desalination, water treatment and food processing applications. In this work, the economic feasibility of a multi-stage RO process including both retentate and permeate reprocessing for the removal of chlorophenol from wastewater is explored using simulation and optimisation studies. Firstly, a mathematical model of the process is developed based on the solution diffusion model, which was validated using experimental chlorophenol removal from the literature, is combined with several appropriate cost functions to form a full model package. Secondly, for a better understanding of the interactions between the different parameters on the economic performance of the process, a detailed process simulation is carried out. Finally, a multi-objective optimisation framework based on Non-Linear Programming (NLP) problem is developed for minimising the product unit cost, the total annualised cost, the specific energy consumption together with optimising the feed pressure and feed flow rate for an acceptable level of chlorophenol rejection and total water recovery rate. The results clearly show that the removal of chlorophenol can reach 98.8% at a cost of approximately 0.21 $/m³

Inherent safety health environment and economic assessment for sustainable chemical process design: Biodiesel case study

Chemical process design involves the development of chemical route that converts the feedstock to the desired product. During chemical process design, the sustainability features, i.e. safety, health and environmental (SHE), and economic performance (EP) should be established through assessment. However, at present, no relevant assessment framework with simultaneous consideration of SHE and EP is reported in literature. As improvement to the mentioned shortfall, this thesis presents four systematic frameworks for chemical process design based on multiple objectives of inherent SHE and EP. These frameworks are specifically dedicated for three design stages of (1) research and development, (2) preliminary engineering stage, and (3) basic engineering stage, and lastly (4) uncertainty analysis with the presence of multiple operational periods. Following the proposed frameworks, the mathematical optimisation models were developed for the assessment. Besides, multi-objective optimisation algorithm (fuzzy optimisation) and multi-period optimisation approach were also integrated into the frameworks to address the multiple objectives, uncertainties and multiple operational periods. To illustrate the frameworks proposed in this thesis, the assessments on biodiesel production pathway in different design stages were solved. Prior to the assessment, eight alternative biodiesel production pathways were identified based on literature. Through the evaluations and assessments in each design stage using the proposed frameworks, a final optimum biodiesel production pathway, i.e. enzymatic transesterification using waste vegetable oil, was designed through assessment. This pathway was further assessed and improved via assessment in basic engineering stage and uncertainty analysis. Following the assessments, several inherent SHE improvement strategies for all the three highlighted design stages were also suggested. Lastly, it can be concluded that the developed frameworks provide simplified yet effective ways for chemical process design based on the multi-objective of inherent SHE and EP

Portfolio Analysis in Supply Chain Management of a Chemicals Complex in Thailand

There is a considerable amount of research literature available for the optimisation of supply chain management of the chemical process industry. The context of supply chain considered in this thesis is the supply chain inside the chemical complex which is the conversion of raw materials into intermediate chemicals and finished chemical products through different chemical processes. Much of the research in the area of planning and scheduling for the process sector has been focused on optimising an individual chemical process within a larger network of a chemicals complex. The objective of this thesis is to develop a multi-objective, multi-period stochastic capacity planning model as a quantitative tool in determining an optimum investment strategy while considering sustainability for an integrated multi-process chemicals complex under future demand uncertainty using the development of inorganic chemicals complex at Bamnet Narong, Thailand as the main case study. Within this thesis, a number of discrete models were developed in phases towards the completion of the final multi-objective optimisation model. The models were formulated as mixed-integer linear programming (MILP) models. The first phase was the development of a multi-period capacity planning optimisation model with a deterministic demand. The model was able to provide an optimal capacity planning strategy for the chemicals complex at Bamnet Narong, Thailand. The numerical results show that based on the model assumptions, all the proposed chemical process plants to be developed in the chemicals complex are financially viable when the planning horizon is more than 8 years. The second phase was to build a multi-period stochastic capacity planning optimisation model under demand uncertainty. A three-stage stochastic programming approach was incorporated into the deterministic model developed in the first phase to capture the uncertainty in demand of different chemical products throughout the planning horizon. The expected net present value (eNPV) was used as the performance measure. The results show that the model is highly demand driven. The third phase was to provide an alternative demand forecasting method for capacity planning problem under demand uncertainty. In the real-world, the annual increases in demand will not be constant. A statistical analysis method named “Bootstrapping” was used as a demand generator for the optimisation model. The method uses historical data to create values for the future demands. Numerical results show that the bootstrap demand forecasting method provides a more optimistic solution. The fourth phase was to incorporate financial risk analysis as constraints to the previously developed multi-period three-stage stochastic capacity planning optimisation model. The risks associated with the different demand forecasting methods were analysed. The financial risk measures considered in this phase were the expected downside risk (EDR) and the mean absolute deviation (MAD). Furthermore, as the eNPV has been used as the usual financial performance measure, a decisionmaking method, named “Minimax Regret” was applied as part of the objective function to provide an alternative performance measure to the developed models. Minimax Regret is one kind of decision-making theory, which involves minimisation of the difference between the perfect information case and the robust case. The results show that the capacity planning strategies for both cases are identical Finally, the last phase was the development of a multi-objective, multi-period three stage stochastic capacity planning model aiming towards sustainability. Multiobjective optimisation allows the investment criteria to be traded off against an environmental impact measure. The model values the environmental factor as one of the objectives for the optimisation instead of this only being a regulatory constraint. The expected carbon dioxide emissions was used as the environmental impact indicator. Both direct and indirect emissions of each chemical process in the chemicals complex were considered. From the results, the decision-makers will be able to decide the most appropriate strategy for the capacity planning of the chemicals complex

Design of biomass value chains that are synergistic with the food-energy-water nexus: strategies and opportunities

Humanity’s future sustainable supply of energy, fuels and materials is aiming towards renewable sources such as biomass. Several studies on biomass value chains (BVCs) have demonstrated the feasibility of biomass in replacing fossil fuels. However, many of the activities along the chain can disrupt the food–energy–water (FEW) nexus given that these resource systems have been ever more interlinked due to increased global population and urbanisation. Essentially, the design of BVCs has to integrate the systems-thinking approach of the FEW nexus; such that, existing concerns on food, water and energy security, as well as the interactions of the BVCs with the nexus, can be incorporated in future policies. To date, there has been little to no literature that captures the synergistic opportunities between BVCs and the FEW nexus. This paper presents the first survey of process systems engineering approaches for the design of BVCs, focusing on whether and how these approaches considered synergies with the FEW nexus. Among the surveyed mathematical models, the approaches include multi-stage supply chain, temporal and spatial integration, multi-objective optimisation and uncertainty-based risk management. Although the majority of current studies are more focused on the economic impacts of BVCs, the mathematical tools can be remarkably useful in addressing critical sustainability issues in BVCs. Thus, future research directions must capture the details of food–energy–water interactions with the BVCs, together with the development of more insightful multi-scale, multi-stage, multi-objective and uncertainty-based approaches