Process synthesis and optimization of a sustainable integrated biorefinery via fuzzy optimization

Over the last decade, utilization of biomasses is highly encouraged to conserve scarce resources, reduce dependency on energy imports as well as protect the environment. Integrated biorefinery emerged as noteworthy concept to integrate several conversion technologies to have more flexibility in product generation with energy self‐sustained and reduce the overall cost of the process. Integrated biorefinery is a processing facility that converts biomass feedstocks into a wide range of value added products via multiple technologies. In this work, a systematic approach for the synthesis and optimization of a sustainable integrated biorefinery which considers economic, environmental, inherent safety, and inherent occupational health performances is presented. Fuzzy optimization approach is adapted to solve four parameters simultaneously as they are often conflicting in process synthesis and optimization of an integrated biorefinery. An integrated palm oil‐based biorefinery case study is solved to demonstrate the proposed approach. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4212–4227, 2013     

A systematic methodology for optimal mixture design in an integrated biorefinery

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.     

Synthesis of sustainable integrated biorefinery via reaction pathway synthesis: Economic,incremental enviromental burden and energy assessment with multiobjective optimization

With the increasing attention toward sustainable development, biomass has been identified as one of the most promising sources of renewable energy. To convert biomass into value‐added products and energy, an integrated processing facility, known as an integrated biorefinery is needed. To date, various biomass conversion systems such as gasification, pyrolysis, anaerobic digestion and fermentation are well established. Due to a large number of technologies available, systematic synthesis of a sustainable integrated biorefinery which simultaneously considers economic performance, environmental impact, and energy requirement is a challenging task. To address this issue, multiobjective optimization approaches are used in this work to synthesize a sustainable integrated biorefinery. In addition, a novel approach (incremental environmental burden) to assess the environmental impact for an integrated biorefinery is presented. To illustrate the proposed approach, a palm‐based biomass case study is solved. © 2014 American Institute of Chemical Engineers AIChE J, 61: 132–146, 2015     

A multi-criteria optimisation approach for the design of sustainable utility systems

Large amounts of gaseous emissions are generated by combustion processes associated with the utility systems. The emissions include SO_x, CO₂, CO, NO_x, CH₄, and N₂O. Such emissions can result in significant impact on the surrounding environment. As a result of serious concerns about environmental problems in recent years, the design criteria for a modern utility system should include both environmental and economic requirements. This work proposes a multi-objective optimisation (MOO) strategy to identify the sustainable design of utility systems that satisfies both economic and environmental goals. A MOO mixed integer linear programming (MILP) model is developed to combine the minimisation of costs with the minimisation of environmental impact that is assessed in terms of life cycle environmental burdens. Most of the gaseous emissions are addressed in the model. The resulting MOO problem is solved using lexicographic goal programming (LGP) techniques. The new strategy has been applied to a case study for the design of a utility system with specific utility demands.     

A mixed integer optimisation approach for integrated water resources management

In areas lacking substantial freshwater resources, the utilisation of alternative water sources, such as desalinated seawater and reclaimed water, is a sustainable alternative option. This paper presents an optimisation approach for the integrated management of water resources, including desalinated seawater, wastewater and reclaimed water, for insular water deficient areas. The proposed mixed integer linear programming (MILP) model takes into account the subdivided regions on the island, the subsequent localised needs for water use (including water quality) and wastewater production, as well as geographical aspects. In addition, the integration of potable and non-potable water systems is considered. The optimal water management decisions, including the location of desalination, wastewater treatment, and reclamation plants, as well as the conveyance infrastructure for desalinated water, wastewater and reclaimed water, are obtained by minimising the annualised total capital and operating costs. Finally, the proposed approach is applied to two Greek islands: Syros and Paros-Antiparos, for case study and scenario analysis.     

A decision support tool for strategic planning of sustainable biorefineries

In this paper we formulate, implement, and test a model for technology and product portfolio design for a multi-product multi-platform biorefining enterprise. The model considered is an MILP financial planning model with the objective of maximizing the stakeholder value. Integer variables are used to select appropriate feedstocks, technologies, and products, material and capacity balances are used to design capacity and set production targets, while cash balances are used to describe investment and operations financing. Stakeholder value is described as the shareholder value with monetized environmental implications in terms of emissions mitigation costs and credits. Process integration schemes utilizing emissions are considered to reduce the emissions load and add to the bottom-line. A preliminary process design and product portfolio is provided as a result. Advantages of process integration are quantified using a central utilities facility and effluent recycles. Sensitivity analysis is conducted to determine important parameters that shape the objective function.     

Strategic optimisation of biomass-based energy supply chains for sustainable mobility

The identification of alternative and sustainable energy sources has been one of the fundamental research goals of the last two decades, and the transport sector plays a key role in this challenge. Electric cars and biofuel fed vehicles may contribute to tackle this formidable issue. According to this perspective, a multi-echelon supply chain is here investigated considering biomass cultivation, transport, conversion into bioethanol or bioelectricity, distribution, and final usage in alternative bifuel (ethanol and petrol) and electric vehicles. Multiperiod and spatially explicit features are introduced in a Mixed Integer Linear Programming (MILP) modelling framework where economic (in terms of Net Present Value) and environmental (in terms of Greenhouse Gases emissions) objectives are simultaneously taken into account. The first and second generation bioethanol production supply chain is matched with a biopower production supply chain assessing multiple technologies. Both corn grain and stover are considered as biomass sources. In the environmental analysis, the impact on emissions caused by indirect Land Use Change (iLUC) effects is also assessed. Results will show the efficacy of the methodology at providing stakeholders with a quantitative tool to optimise the economic and environmental performance of different supply chain configurations.     

Process analysis and optimisation of hybrid processes for the dehydration of ethanol

Promising hybrid processes for ethanol dewatering consist of different combinations of distillation with adsorption and/or vapour permeation. This paper presents an analysis and optimisation of these hybrid processes using non-equilibrium models and an evolutionary algorithm. Four different membrane assisted configurations are compared with a benchmark process consisting of distillation and pressure swing adsorption. In total 12 cases were investigated while assuming different feed and product compositions at different production capacities: three ethanol mass fractions in feed 45, 80, 92 wt.%, two product purities 99.6, 99.95 wt.% and two production capacities 25,000, 250,000 m³/year. The influence of decisive operating and structural variables on important target variables such as total membrane area is demonstrated. Finally, the processes are evaluated regarding operating costs and energy consumption depending on product purity and production capacity. The operating costs of the membrane assisted configurations differ only in a small range of −3% to 6% from those of the benchmark. The energy consumption of the membrane assisted configurations without distillation is up to 30% lower compared to the benchmark. Especially the combination of vapour permeation and adsorption is a promising alternative allowing for producing ethanol with high purities at lower operating pressures compared to the vapour permeation as stand alone process.     

Sustainable design and synthesis of algae‐based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration

The superstructure optimization of algae‐based hydrocarbon biorefinery with sequestration of CO₂ from power plant flue gas is proposed. The major processing steps include carbon capture, algae growth, dewatering, lipid extraction and power generation, and algal biorefinery. We propose a multiobjective mixed‐integer nonlinear programming (MINLP) model that simultaneously maximizes the net present value (NPV) and minimizes the global warming potential (GWP) subject to technology selection constraints, mass balance constraints, energy balance constraints, technoeconomic analysis constraints, and environmental impact constraints. The model simultaneously determines the optimal decisions that include production capacity, size of each processing unit, mass flow rates at each stage of the process, utility consumption, economic, and environmental performances. We propose a two‐stage heuristic solution algorithm to solve the nonconvex MINLP model. Finally, the bicriteria optimization problem is solved with ε‐constraint method, and the resulting Pareto‐optimal curve reveals the trade‐off between the economic and environmental criteria. The results show that for maximum NPV, the optimal process design uses direct flue gas, a tubular photobioreactor for algae growth, a filtration dewatering unit, and a hydroprocessing pathway leading to 47.1 MM gallons of green diesel production per year at $6.33/gal corresponding to GWP of 108.7 kg CO₂‐eq per gallon. © 2013 American Institute of Chemical Engineers AIChE J, 59: 1599–1621, 2013     

A shortcut method for the preliminary synthesis of process-technology pathways: An optimization approach and application for the conceptual design of integrated biorefineries

Synthesis and screening of technology alternatives is a key process-development activity in the process industries. Recently, this has become particularly important for the conceptual design of biorefineries. This work introduces a shortcut method for the synthesis and screening of integrated biorefineries. A structural representation (referred to as the chemical species/conversion operator) is introduced. It is used to track individual chemicals while allowing for the processing of multiple chemicals in processing technologies. The representation is used to embed potential configurations of interest. An optimization approach is developed to screen and determine optimum network configurations for various technology pathways using simple data. The solution to the optimization formulation provides a quick and effective method for screening and interconnecting the technological pathways and to distributing the flows over the network. Case studies are solved to illustrate the applicability of the proposed approach.     

Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries

Climate change mitigation has become a binding driver in biofuels production. First generation bioethanol, initially indicated as the most competitive option, is now incurring in ever increasing discredits forcing the transition towards more sustainable productions (i.e. second and third generation technologies). This paper addresses the strategic design and planning of corn grain- and stover-based bioethanol supply chains through first and second generation technologies. A Mixed Integer Linear Programming framework is proposed to optimise the environmental and financial performances simultaneously. Multi-period, multi-echelon and spatially explicit features are embodied within the formulation to steer decisions and investments through a global approach. A demonstrative case study is proposed involving the future Italian biomass-based ethanol production. Results show the effectiveness of the optimisation tool at providing decision makers with a quantitative analysis assessing the economic and environmental performance of different design configuration and their effect in terms of technologies, plant sizes and location, and raw materials.     

The multi-period optimisation of an amine-based CO2 capture process integrated with a super-critical coal-fired power station for flexible operation

In this work, we present a model of a super-critical coal-fired power plant integrated with an amine-based CO₂ capture process. We use this model to solve a multi-period dynamic optimisation problem aimed at decoupling the operation of the power plant from the efficiency penalty imposed by the CO₂ capture plant, thus providing the power plant sufficient flexibility to exploit price variation within an electricity market. We evaluate four distinct scenarios: load following, solvent storage, exhaust gas by-pass and time-varying solvent regeneration. The objective is to maximise the decarbonised power plant's short run marginal cost profitability. It is found that while the solvent storage option provides a marginal improvement of 4% in comparison to the load following scenario, the exhaust gas bypass scenario results in a profit reduction of 17% whereas the time-varying solvent regeneration option increases the profitability of the power plant by 16% in comparison to the reference scenario.     

A review of the potential of pretreated solids to improve gas biofuels production in the context of an OFMSW biorefinery

The organic fraction of municipal solid waste (OFMSW), mainly composed of lignocellulosic polymers, is extremely complex. Therefore, it is necessary to apply pretreatments to remove the lignin content and decrease the cellulose crystallinity in order to use the OFMSW for gas biofuels production in the context of biorefineries from waste. This work focused on critically reviewing the conventional pretreatments applied to OFMSW, with the goal of improving the H₂ production, as well as other biofuels in modern biorefineries. There are a wide variety of pretreatments that have successfully been used, mainly alkaline, milling and dilute acid. In addition, some research has focused on the recovery and reutilization of the alkali, acid or solvents after the pretreatment, to be incorporated into new cycles of production, minimizing the environmental impacts. Moreover, it would be necessary to incorporate analytical tools, in order to determine the sustainability of the biorefinery project. It is concluded that waste pretreatments could significantly contribute to increased yields of biogas fuels in organic waste‐based biorefineries. Therefore, establishing preliminary stages for conditioning biomass or wastes is essential to improve the degradation of wastes and bio‐product generation. © 2016 Society of Chemical Industry     

Heuristic framework for the debottlenecking of a palm oil-based integrated biorefinery

This paper presents a heuristic framework that can be used for the debottlenecking of a palm oil-based integrated biorefinery with multiple processes and products. In current industrial practice, any individual unit within these systems is generally designed for a required size. Besides, it also takes into account of an additional margin for safety to meet the requirement of the baseline state of the process. In case there is a variation in the quality of the supplied feedstock or an increase for product demand, it becomes necessary to identify the bottleneck process unit in order to handle the new variation and meet all requirements. In response, the system has to be debottlenecked by altering important operating parameters from the baseline state that limits the change. This stage entails formulating and solving a detailed model for this particular process. In this paper, frameworks are presented to aid decision makers to first identify a bottleneck and subsequently debottleneck the process. The frameworks are essentially a guide for design and safety engineers for decision making at conceptual design stage. A design stage palm oil-based integrated biorefinery case study is solved to demonstrate the proposed approach.     

A multiobjective optimization framework for design of integrated biorefineries under uncertainty

A systematic approach for development of a reliable optimization framework to address the optimal design of integrated biorefineries in the face of uncertainty is presented. In the current formulation, a distributed strategy which is composed of different layers including strategic optimization, risk management, detailed mechanistic modeling, and operational level optimization is applied. In the strategic model, a multiobjective stochastic optimization approach is utilized to incorporate the tradeoffs between the cost and the financial risk. Then, Aspen Plus models are built to provide detailed simulation of biorefineries. In the final layer, an evolutionary algorithm is employed to optimize the operating condition. To demonstrate the effectiveness of the framework, a hypothetical case study referring to a multiproduct lignocellulosic biorefinery is utilized. The numerical results reveal the efficacy of the proposed approach; it provides decision makers with a quantitative analysis to determine the optimum capacity plan and operating conditions of the biorefinery. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3208–3222, 2015     

Multi-objective optimisation using surrogate models for the design of VPSA systems

Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO₂/N₂ separation. A 5 times reduction in computational effort is observed.     

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Large scale engineering process systems are subject to a variety of risks which affect the productivity and profitability of the industry in the long run. This paper outlines the short comings of the current methods of risk quantification and proposes a systems engineering framework to overcome these issues. The functionality of the developed model is illustrated for the case of mineral processing and metal production industries using a copper ore processing and refined metal production case study. The methodology provides a quantitative assessment of the risk factors and allows the opportunity to minimise financial losses, which would help investors, insurers and plant operators in these sectors to make appropriate risk hedging policies. The models developed can also be coupled with evolutionary or swarm based algorithms for optimising the systems. A numerical example is illustrated to demonstrate the validity of the proposition.