Process Network Synthesis Research Articles

Evaluation of technology structure based on energy yield from wheat straw for combined bioethanol and biomethane facility

The objective of this paper is to evaluate a combined bioethanol and biomethane production from wheat straw applying process network synthesis for maximised energy yield per ton lignocellulosic biomass input. Experimental results in combination with literature data were implemented in process synthesis software for energy optimisation. Wheat straw was steam exploded at different pretreatment conditions on laboratory scale. Glucose and ethanol yields as well as specific biogas yields of different solid and liquid fractions were determined via batch-experiments. Preferable pretreatment conditions differ between biogas and bioethanol production. The optimal process configuration was found to consist of direct biogas production from steam explosion pretreated straw at 170 °C for 20 min combined with bioethanol production from straw pretreated at 200 °C for 20 min. This process results in a purified methane yield of 7892 MJ and a purified ethanol yield of 964 MJ per ton untreated straw input.

Process synthesis involving multi-period operations by the P-graph framework

The P-graph (process graph) framework is an effective tool for process-network synthesis (PNS). Here we extended it to multi-period operations. The efficacy of the P-graph methodology has been demonstrated by numerous applications. The unambiguous representation of processes and the availability of the axioms defining combinatorially feasible structures facilitates the development of efficient algorithms to determine maximal structures, solution structures, and optimal structure for processes. However, it is presumed that single-period operation prevails. It implies that the operating conditions and the load of each operating unit remain unchanged, i.e., steady-state operation. This is usually true for the chemical industry but often not in agriculture or where seasonal effects are important. The current work proposes a multi-period operation wherein the load of operating units varies from period to period to accommodate demand, assuming operating conditions remain steady in each period. A modeling technique is proposed to represent operating units in the multi-period operation. The different periods are connected by “fictitious” streams, which ensures, that the unit is sized properly.

Performance enhancement and energy reduction using hybrid cryogenic distillation networks for purification of natural gas with high CO2 content

A novel concept of hybrid cryogenic distillation network has been explored which maximizes the benefits of both desublimation or solid-vapor based separation as well as distillation or vapor-liquid equilibrium based separation during the separation of carbon dioxide from methane or natural gas. Process network synthesis has been performed for four case studies with high carbon dioxide (72 mole%) and medium carbon dioxide (50 mole%) natural gas feed streams. The benefits of optimal locations for cryogenic packed beds were investigated. A conventional cryogenic network consisting of multiple distillation columns with butane as additive for extractive distillation was also studied and presented in this paper. Process modeling of cryogenic distillation network with MESH equations was attempted using an integrated dual loop (C+3) convergence and the results were compared with Aspen Plus simulator for benchmarking. The prediction of solidification region was employed using experimental data from literature to avoid solidification regions in the column. The proposed hybrid cryogenic distillation network showed promising potential for energy and size reduction.

Optimal renewable energy systems for industries in rural regions

BackgroundEnergy systems for smart regions will require on the one hand a much higher share of renewable energy sources for heat and electricity and on the other hand a high standard integration of industry and utilities supplying households and businesses. As energy options become more diverse, integration of industry into the energy provision for settlements and vice versa become important from an economic and ecological point of view. A particular problem is the supply of bio-energy to large industrial complexes as this requires considerable efforts to manage resource provision as well as constructing smart systems to utilise available resources without posing direct competition to the supply of food and feed.MethodsThis paper will discuss the application of Process Network Synthesis (PNS) using the P-Graph method as a tool to generate optimal energy systems supplying industry with energy based on bio-resources in the context of a rural region and small urban centre. Application of these methods to a real life case study in an industrial city in rural context (Weiz in Austria) will be discussed from the viewpoint of general lessons to be learned from the design of complex energy systems linking different energy provision sources to supply industry and residential areas in a systemic way with energy.ResultsThe synthesis resulted in a basic optimal solution containing the economically most feasible regional energy-technology network. Scenarios show differences in resource use and the creation of revenue. Anaerobic digestion, CHP and wood gasification appear for most of the scenarios.ConclusionsThe case study shows that a support of large consumers with sustainable regional energy systems can already be realised with current market prices. Heat demand outside industry critically influences the economic success of regional technology networks. Feed-in tariffs remain as a key factor for the creation of revenue. As regional parameters like resource availability, prices and locations where technologies could be situated change, the economic success also changes. Price limits and stable technologies can be identified.

ϵ-OA for the solution of bi-objective generalized disjunctive programming problems in the synthesis of nonlinear process networks

There has been an increasing interest in multicriteria optimization (MCO) of nonlinear process network problems in recent years. Several mathematical models have been developed and solved using MCO methodologies including ϵ-constraint, weighted sum, and minimum distance. In this paper, we investigate the bi-objective nonlinear network synthesis problem and propose an effective algorithm, ϵ-OA, based on augmented ϵ-constraint and logic-based outer approximation (OA). We provide theoretical characterization of the proposed algorithm and show that the solutions generated are efficient. We illustrate the effectiveness of our novel algorithm on two benchmark problems. The ϵ-OA is compared to the straightforward use of OA with augmented ϵ-constraint algorithm (ϵ-con+OA), the augmented ϵ-constraint without OA (ϵ-MINLP), and the traditional ϵ-constraint (T-ϵ-con). Based on the results, our novel algorithm is very effective in solving the bi-objective generalized disjunctive programming problems in the synthesis of process networks.

Systematic network synthesis and design: Problem formulation, superstructure generation, data management and solution

The developments obtained in recent years in the field of mathematical programming considerably reduced the computational time and resources needed to solve large and complex Mixed Integer Non Linear Programming (MINLP) problems. Nevertheless, the application of these methods in industrial practice is still limited by the complexity associated with the mathematical formulation of some problems. In particular, the tasks of design space definition and representation as superstructure, as well as the data collection, validation and handling may become too complex and cumbersome to execute, especially when large problems are considered. In an earlier work, we proposed a computer-aided framework for synthesis and design of process networks. In this contribution, we expand the framework by including methods and tools developed to structure, automate and simplify the mathematical formulation of the design problem. Furthermore, the models employed for the representation of the process alternatives included in the superstructure are refined, through the inclusion of the energy balance. Finally, the features of the framework are highlighted through the solution of two case studies focusing on food processing and biofuels.

Optimal Renewable Energy Systems for Regions

Most sources for renewable energy can be deduced from solar radiation as the main natural income of society. Contrary to conventional fossil and radioactive energy resources that are mined or pumped out from central point sources, solar energy is a de-central resource that requires area for its conversion to useful products and services. This requires a new technological as well as logistical concept for energy systems where regions play a key role as providers of energy and goods. The contribution will provide the conceptual framework for renewable energy system generation on a regional level, taking into account the responsibility of regions to provide goods and services to the larger society and to support urban centres. It will show how optimal resource-technology-demand networks may be constructed, using process network synthesis approaches and how the ecological efficiency of such regional systems can be measured. Application of these methods to real life case studies (in particular the region of Muhlviertel in Austria) will on the one hand prove the versatility of the methods presented and on the other hand will provide insight into the scope of necessary change if society moves towards a low carbon sustainable energy system.

Reliability considerations for process network synthesis problems

A manufacturing system consists of operating units which convert their input materials into their output materials. In the problem of designing a process network, we have to find a suitable network of operating units which produces the desired products from the given raw materials. If we consider this process network design problem from a structural point of view, then minimizing the cost of the network leads to an NP-complete combinatorial optimization problem called Process Network Synthesis or PNS problem. In this paper we define an extension of the PNS problem, where probability of failure, operating time and repair time are assigned to the operating units, and some costs are assigned to the system. We provide algorithms that determine the probability of system failure, the operating units which should be used, and the scheduling of the reparation of operating units in order to obtain expected optimal operation of the system.

Building-evacuation-route planning via time-expanded process-network synthesis

A method and software are proposed for optimal building-evacuation-route planning in terms of identifying evacuation routes and scheduling of evacuees on each route. First, the building-evacuation routes are represented by a P-graph, which gives rise to a time-expanded process-network synthesis (PNST) problem that can be algorithmically solved by the P-graph framework. In the proposed method, each location and passage in the building is given by a set of attributes to be taken into account in the evacuation-route planning. The evacuation time is calculated as a minimum cost of the corresponding PNST. In addition to the globally optimal solution, the P-graph framework provides the n-best sub-optimal solutions. The validity of the proposed method is illustrated by two examples.

Generating and Analyzing Mathematical Programming Models of Conceptual Process Design by P-graph Software

The primary aim of process-network synthesis, or PNS in short, is to determine the best process network achieving a desired goal, e.g., producing a set of desired products or satisfy demands. PNS has a long history, and numerous methods for executing it are available. Its acceleratedly increasing importance can be attributed to the need to respond to the rapid emergence of new technologies and fast changes in the economic environment. It is highly desirable that any corporation be able to ascertain if a new technology is viable for its business as well as to assess if its current technology remains sustainable in the changing environment. Herein, a novel method and software for PNS are proposed for generating, optimizing, and analyzing alternative process designs at the conceptual level. The method is illustrated by synthesizing alternative process designs with different network structures for the production of butanol, ethanol, and acetone from grains. Furthermore, the sustainability of the resultant pro...

Extending Process-Network Synthesis Algorithms with Time Bounds for Supply Network Design

It is highly desirable to develop a software for estimating the quality of supply networks and synthesizing potential alternative realizations to improve the quality of service or to satisfy service level agreements. In the present paper, methodology, algorithm, and software are proposed to improve supply networks where the quality is measured by cost and response time. The methodology is based on the combinatorial foundations of algorithmic process synthesis. Structure generation and evaluation of a supply scenario are the two major steps of the algorithm elaborated for synthesizing the optimal network structure. The algorithm, besides generating alternative structures, takes into account alternative orders of the activities as well. The structural alternatives are evaluated on the basis of the cost and duration of each individual operation. The cost and response time for a single operation are expressed as concave functions of its volume.

Ecological and economic evaluation of biogas from intercrops

BackgroundBiogas made from main crops (e.g., corn) is commonly used for producing electricity and heat. Nevertheless, the production of energy from monocultures is highly unsustainable and not truly renewable. Since neither monocultures nor food competition are desirable, intercrops can be used to increase the yield per hectare instead of leaving agricultural fields unplanted for soil regeneration. The extra biomass can be used for biogas production. In a case study, the economic as well as the ecological feasibility of biogas production using intercrops, cattle manure, grass and corn silage as feedstocks for fermenters was analyzed.MethodsThe set-up for the case study included different feedstock combinations as well as spatial distributions of substrate supply and heat demand for modeling and optimization. Using the process network synthesis, an optimum structure was generated representing the most economical technology constellation which included transport of substrates, heat and biogas (when applicable). The ecological evaluation was carried out by using the sustainable process index method.ResultsThe application of both methodologies to different scenarios allowed a constellation to be found which is economically feasible while entailing low ecological pressure. It is demonstrated that the production of intercrops for producing biogas has so far not been regarded as a viable option by the farmers due to a variety of barriers. Sensitization is needed to emphasize that planting intercrops holds many advantages like positive effects on soil regeneration and raised nitrogen fixation, as well as increased biomass output per hectare and, last but not least, it allows the production of energy without conflicts between food and energy production.ConclusionsUsing intercrops for the production of biogas has the potential to decrease the ecological footprint decisively while still offering opportunities in the lucrative biogas market. The transfer of know how regarding this option should be taken up by agricultural training.

Clean fuel technologies and clean and reliable energy: a summary

There are two major areas covered by this current Special Issue: Cleaner Fuel Technologies and Waste Processing. In addition, the Special Issue, also includes some recent developments in various fields of energy efficiency research. The first group of contributions considers in detail, hydrogen production from biomass and hydrogen production by the sorption-enhanced steam methane reforming process (SE-SMR). Biomass-related technologies are also discussed for a design of an integrated biorefinery, production of clean diesel fuel by co-hydrogenation of vegetable oil with gas oil and utilization of microwave and ultrasound pre-treatments in the production of bioethanol from corn. Waste Processing aspects are considered in the second group of papers. This section includes integrated waste-to-energy plants, utilisation of municipal solid waste in the cement industry and urban supply and disposal systems. The third topic is intentionally made rather loose: it includes different research topics on various aspects of energy efficiency, e.g. resource-saving network design, new research on divided wall columns, vehicle logistics as process-network synthesis for energy consumption and CO2 reduction. Cleaner fuel technologies section

Disjunctive-Genetic Programming Approach to Synthesis of Process Networks

In this work, disjunctive-genetic programming (D-GP), based on the integration of genetic algorithm (GA) with the disjunctive formulations of generalized disjunctive programming (GDP) for the optimization of process networks, has been proposed. Discrete optimization problems, which give rise to the conditional modeling of equations through representations as logic based disjunctions, are very important and often appear in all scales of chemical engineering process network design and synthesis. The decomposition of the resulting mixed integer nonlinear programming (MINLP) problem obtained from the relaxation of the disjunctive formulation of the original problem results in alternating solutions of mixed integer linear programming (MILP) master problems and nonlinear programming (NLP) subproblems. With the increase in the problem scale, dealing with such alternating routes becomes difficult due to increased computational load and possible entanglement of the results in suboptimal solutions due to infeasibilities in the MILP space. In this work, the genetic algorithm (GA) has been used as a jumping operator to the different terms of the discrete search space and for the generation of different feasible fixed configurations. This proposed approach eliminates the need for the reformulation of the discrete/discontinuous optimization problems into direct MINLP problems, thus allowing for the solution of the original problem as a continuous optimization problem but only at each individual discrete and reduced search space. Segment-based mutation (SBM) and segment-based floating crossover (SBFC) strategies were proposed for the efficient handling of the population of chromosomes comprising the coded terms of the disjunctions. The effectiveness of D-GP as shown on various benchmark problems involving heat exchange equipment design and network problems as well as a classical process network problem shows this may be a promising approach in dealing with process network problems with large discontinuous objective and/or constraint functions.

Optimal design of supply chains by P-graph framework under uncertainties

The utilization of renewable resources poses new challenges to process design. Consumption of renewable raw materials, or feedstock, often encounter limitations and uncertainties that are foreign to the design of processes based on fossil fuels whose availability is presumably uninterruptible. The algorithms and software of the graphtheoretic method based on process graphs (P-graphs) are elaborated for generating optimal and near-optimal supply networks for manufacturing a set of products at certain production levels with given reliabilities. Apparently, the sole currently available approach capable of rigorously and efficiently executing optimal process-network synthesis is that based on the P-graph framework. The approach leads to an algorithmically and mathematically proven solution for all steps involved. Such steps comprise superstructure generation, construction of the mathematical model, optimization, and the solution interpretation. The proposed method is illustrated with an example involving the utilization of renewable feedstock, namely agricultural products, whose uninterrupted availability tends to be uncertain.

Solving vehicle assignment problems by process-network synthesis to minimize cost and environmental impact of transportation

A method and software are proposed for optimal assignment of vehicles to transportation tasks in terms of total cost and emission. The assignment problem is transformed into a process-network synthesis problem that can be algorithmically handled by the P-graph framework. In the proposed method, each task is given by a set of attributes to be taken account in the assignment; this is also the case for each vehicle. The overall mileage is calculated as the sum of the lengths of all the routes to be travelled during, before, after, and between the tasks (Desaulniers et al. 1998; Baita et al. 2000). Cost and emission are assigned to the mileages of each vehicle type. In addition to the globally optimal solution of the assignment problem, the P-graph framework provides the n-best suboptimal solutions that can be ranked according to multiple criteria. The viability of the proposed method is illustrated by an example.

Solution of separation-network synthesis problems by the P-graph methodology

The current work demonstrates that separation-network synthesis (SNS) problems can be transformed into process-network synthesis (PNS) problems: the SNS problems constitute a particular class of PNS problems. Thus, the transformed SNS problems are solvable by resorting to the P-graph methodology originally introduced for the PNS problems. The methodology has been unequivocally proven to be inordinately effective.

Reduction techniques for the PNS problems: a novel technique and a review

Process Network Synthesis (PNS) has an enormous practical impact. The problem is very difficult to solve, determining the cost optimal network of operating units with fixed charge belongs to the complexity class of NP-hard problems. Therefore, it is important to develop reduction algorithms to minimize the size of the problem. In the present work the available reduction techniques for PNS problems are reviewed as well as a further reduction algorithm is presented. The performance of the new algorithm is examined by empirical analysis.

Price-Targeting Through Iterative Flowsheet Syntheses in Developing Novel Processing Equipment: Pervaporation

Novel processing equipment, i.e., an operating unit, such as any of various separators, is continually under development for commercialization. An innovative strategy has been proposed to establish the economically viable target price of such an operating unit through iterative flowsheet syntheses by parametrically reducing its current price. The proposed strategy resorts to a P-graph- (process-graph-) based algorithmic method for process-network synthesis. The strategy has been demonstrated to be highly efficient by determining the target price of the pervaporation unit for downstream processing of biochemically manufactured butanol, ethanol, and acetone. It is worth noting that the proposed strategy for price targeting in developing a novel and immature operating unit, such as pervaporation, is fundamentally different from that involved in retrofitting, which involves only the mature operating units at their current costs.

An improved piecewise outer-approximation algorithm for the global optimization of MINLP models involving concave and bilinear terms

In this paper a new version of the Outer Approximation for Global Optimization Algorithm by Bergamini et al. [Bergamini, M.L., Aguirre, P., & Grossmann, I.E. (2005a). Logic based outer approximation for global optimization of synthesis of process networks. Computers and Chemical Engineering 29, 1914] is proposed, in order to speed up the convergence in nonconvex MINLP models that involve bilinear and concave terms. Bounding problems are constructed replacing these nonconvex terms by piecewise linear underestimators. These problems, which correspond to mixed-integer linear programs, are solved to generate approximate solutions with improved objective value. When no further feasible solution can be found, this guarantees that the upper bound cannot be improved in the nonconvex problem, thus providing a termination criterion. The new algorithm is applied to five different synthesis problems in the areas of water networks, heat exchanger networks and distillation sequences. The results show a significant reduction in the computational cost compared with the previous version of the algorithm.