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Abstract: Utilization of biomass through anaerobic digestion and hydrothermal carbonization is crucial to maximize resource efficiency. At the same time, supply chain integration ensures sustainable feedstock management and minimizes environmental and logistical impacts, enabling a holistic approach to a circular bioeconomy. This study presents an integrated approach to simultaneously optimize the biomass supply chain network and process flowsheet, which includes anaerobic digestion, cogeneration, and hydrothermal carbonization. A three-layer supply chain network superstructure was hence developed to integrate the optimization of process variables with supply chain features such as transportation modes, feedstock supply, plant location, and demand location. A mixed-integer nonlinear programming model aimed at maximizing the economic performance of the system was formulated and applied to a case study of selected regions in Slovenia. The results show a great potential for the utilization of organic biomass with an annual after tax profit of 23.13 million USD per year, with the production of 245.70 GWh/yr of electricity, 298.83 GWh/yr of heat, and 185.08 kt/yr of hydrochar. The optimal configuration of the supply chain network, including the selection of supply zones, plant locations and demand locations, transportation links, and mode of transportation is presented, along with the optimal process variables within the plant.
Keywords: anaerobic digestion, hydrothermal carbonization, supply chain optimization, process flowsheet optimization, mathematical programming
Published in DKUM: 07.02.2025; Views: 0; Downloads: 6
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Abstract: This paper discusses the influence of the clearance in joints on the joint reaction forces in mechanisms. By using mathematical programming, the optimal parameters of kinematic chains can be efficiently obtained by using the deterministic approach. However, the situation becomes more sophisticated if random effects of tolerances of the arm lengths and the random pin positions have to be considered. In this work the influence of clearances on joint forces is calculated by using the Taylor approximation and the Monte Carlo method. The implementation of the model is illustrated with two examples. The first example considers a closed loop chain, representing a four-bar mechanism being an actual part of a hydraulic support, employed in mining industry. The second example considers joint reaction forces of car wiper mechanism.
Keywords: joint forces, kinematic chain, mathematical programming, stochastic model
Published in DKUM: 12.07.2017; Views: 1024; Downloads: 438
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Abstract: Economically viable process designs should be, in addition to other criteria, profitable over their entire process lifetimes not only at the present time. An improved process design can be achieved by establishing an appropriate trade-off between product income, raw material, operating costs, and investment. The full lifetime of the processes and future prices have to be considered rather than optimising them on a yearly basis using current prices. Single-period optimisation and synthesis models for processes reflects current prices only. The prices can fluctuate rather quickly and the optimal solution may be very different from one year to the another. Therefore, the traditional superstructural synthesis approach applying a mixed-integer nonlinear programming model was upgraded: i) over time, by considering an entire lifetime, which can be described by a multi-period model and ii) the whole field of variation regarding uncertain future prices. A stochastic approach considering the statistical distribution of price projections over an entire lifetime was used on different case studies instead of the traditional deterministic approach accounting for nominal future price projection. The objective was the maximisation of the expected net present value of a process or the expected incremental net present value of different process subsystem. The heat exchanger network has been one of the subsystem, which can significantly contribute to operating costs due to savings of external utility consumption. For this subsystem a deterministic and stochastic multi-period mixed-integer nonlinear programming (MINLP) synthesis models have been developed in order to account for future price projections. Considering higher energy prices gives rise to larger initial investments compared to solutions obtained with current prices. However, due to the uncertainties of utility prices' forecasts, retrofitting using an extension of HEN during future years of the lifespan might be a better strategy. The objective is to identify a design that is the most suitable for effective future extensions and preferably with the lowest sensitivity to energy price fluctuations, as there can be various designs featuring similar initial investment. The results supports that it is economically beneficial to consider future utility prices as the incremental investment is not only paid-off but additional savings are achieved. Process-to-process Heat Integration can also significantly affect the trade-off between investment and operating cost. The aim of Total Site (TS) HEN synthesis was to develop a model synthesis for the TS that, besides many other important features, would also consider future utility prices. Two strategies for TS synthesis have been developed: i) sequential, when HI is performed within a process during the first step and then after a process-to-process HI has been performed, and ii) simultaneous, where the HI is performed within and between processes simultaneously. The second strategy can reveal additional opportunities for heat recovery that might not be identified when applying the first strategy. Comparison of the results obtained at consideration of current utility prices and forecasted utility prices indicates that is worth to account for future utility prices. The separation processes also consume a significant amount of energy. The synthesis of a distillation column sequence integrated within its heat exchanger network was used as a case study for the separation of a multi-component stream into pure component products by considering future utility prices. This analysis has been performed in order to evaluate the magnitude of the influence of forecasted utility prices. It can be concluded that forecasted utility prices can be beneficial, however, the technical limits of the systems should be carefully observed. The price fluctuation can also be observed for other prices not only utility prices, e.g. raw material cost, product price, etc
Keywords: future prices, forecasted prices, stochastic optimisation, mathematical programming, Heat Exchanger Network, Total Site, distillation column sequence, methanol production
Published in DKUM: 04.05.2015; Views: 2190; Downloads: 177
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Keywords: chemical process engineering, process design, optimization of processes, mathematical programming, pinch analysis, waste minimization
Published in DKUM: 01.06.2012; Views: 2472; Downloads: 39
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Abstract: This thesis presents two different approaches to the synthesis of regional networks for biomass and biofuel production and supply: Mathematical Programming and Graph Theoretic approach. The optimisation criterion for both approaches is the maximisation of profit. The first approach is based on a generic optimisation model of biomass production and supply networks. This superstructure approach is based on a flexible number of network layers: plantation, collection using a pre-treatment, process, and consumption. A Mixed Integer Linear Programming (MILP) model has been successfully developed during this work. However, the solution of this biomass production network model is very challenging due to the large sizes of the networks and the number of interconnections. The huge number of redundant variables reduces model efficiency (time taken to solve the model and the interpretation of the results). This model when representing very large size networks cannot be solved over a reasonable time even by professional mathematical programming software tools. Several model-size reduction techniques are therefore proposed for the solution of large-scale networks. In particular, methods are proposed for (i) reducing the connectivity within a biomass supply chain network by setting the maximum allowable distance between the supply zones to the collection centres, (ii) eliminating unnecessary variables and constrains to reduce the zero-flows in the full model, and (iii) aggregating the network and hence the synthesis process by merging the collection centres. The network synthesis is also carried out by P-graph (Process Graph) tools. P-graph is a directed bipartite graph, having two types of vertices — one for operating units and another for those objects representing material or energy flows/quantities. In this procedure, firstly a maximum feasible superstructure for biomass production network is generated from which the optimal structure is then selected by the Branch and Bound method. This graph-based method clearly shows where, how, and what kind of material and energy carriers will be transferred from one supply chain layer to another. In order to test the efficiency of the model, a small regional renewable network problem was solved using both methods. Their performances were tested and the results confirmed the applicability on a regional scale. The proposed model-size reduction techniques were also tested. A large-scale regional case study was created to demonstrate these techniques. The results are very positive and some suggestions for future work are given in the conclusion.
Keywords: Biomass and bioenergy network synthesis, Model-size reduction techniques, Mathematical Programming, MILP, P-Graph
Published in DKUM: 06.01.2011; Views: 3682; Downloads: 137
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