Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing Green Energy and Environment

In the quest for a green economy, bioenergy has become a central component due to its ability to minimize depletion of natural energy resources and enhance environmental sustainability. However, the integration of bioenergy for a green economy has often led to policy resistance, the tendency for solutions to cause disastrous side effects on other aspects of the system that were not envisaged. The use of integrated model-based approaches for selection, design, and analysis of technological alternatives for bioenergy production would significantly enhance the systems’ sustainability by optimizing design and operation, improving growth and profitability, and enabling a more synergistic interaction between the engineering and the macroeconomic aspects of bioenergy production systems. This chapter is designed to develop model-based methodological frameworks that will support sustainable decision making by all stakeholders involved in the design, operation, and commercialization of bioenergy production systems. Practical case studies are presented for bioethanol, biomethane, and synthetic gas production

Integrated Model-Based Frameworks for Synthesis of Anaerobic Treatment Process: Optimizing Operation Using Reactor Networks

Anaerobic treatment technology offers great potential towards achieving Sustainable Development Goals due to its ability to simultaneously breakdown pollutants, generate renewable bioenergy and recycle valuable nutrients from organic waste streams. However, the successful operation of anaerobic digestion (AD) process requires design of optimal process configurations that are well adapted to the characteristics of feedstock available. Due to the highly complex nature of AD, involving multiple reactions with each catalysed by specific groups of microorganisms, multistage anaerobic digestion, in which multiple digesters are operated in a network configuration becomes highly invaluable. This is because such network configurations can optimize overall performance of the AD process by ensuring that the specific conditions under which each reaction step takes place is optimized. In addition, each digester has unique characteristics often making them more adapted to treat waste of specific characteristics than others, and thus utilizing one digester in one configuration may limit the possible combination of pathways, hence limiting overall performance. Model-based design of anaerobic digesters is particularly important as the kinetics captured by AD models can predict operating conditions, volumetric gas production, process stability as well as effluent quality. In addition, systematic model-based approaches for design of anaerobic digestion systems significantly reduce the number of expensive prototype systems and time-consuming studies usually required to obtain and optimal configuration of anaerobic digesters. Even though there exist several studies that use kinetic models to guide design of anaerobic digesters, published literature has primarily been geared towards describing the process of developing a given model to guide design of single stage digester configurations. Remarkably, little research has been carried out on model reliability analysis, especially for the synthesis of multistage digester configurations. This thesis therefore provides both the theoretical background and illustrations (with practical application cases) for development and use of systematic model-based frameworks to guide design and operation of multistage anaerobic digesters irrespective of the information available to the designer. The study uses a methodological approach that develops synergy by systematically coupling model-based techniques (multicriteria decision making tools, practical identifiability, Monte Carlo simulation, adjoint based gradient optimization and attainable region theory) in an optimal framework for synthesis and optimization of anaerobic digester networks. The result of the approach is an optimal framework (decision support system) for synthesis and optimisation of anaerobic digester networks under four practical scenarios: (a) Synthesis based on model requirements or characteristics whereby the study considered the case of no model availability, one-stage kinetic models, two-stage kinetic models, kinetic uncertainty as well as changes in kinetic model structure; (b) Synthesis based on operational/ process objectives, whereby the study considered process stability and process performance (measured in terms of biogas production and organic matter reduction) as design objectives; (c) Synthesis based on economic objectives whereby the study developed digester economic evaluation models based on known economic feasibility indicators as well as macroeconomic parameters; and (d) Synthesis based on feedstock characteristics whereby the study considered two classes of organic substrates (industrial wastewater and animal manure) and analysed the effect of substrate characteristics on the performance targets and optimal configuration of anaerobic digester networks. The results have been captured in five journal publications with the contribution from each paper summarized as follows: Paper 1 presents a framework that uses two-stage kinetic models and process objectives for digester synthesis (mainly methane productivity and volatile solids reduction) while considering the effects of substrate characteristics, using five types of animal manure. The results illustrate that a change in digested substrate significantly influences the operating limits (defined by the attainable region), optimized parameter, as well as the design configuration of the optimal digester structure. This observed substrate effect on attainable regions shows great promises as it paves the way for other substrates such as blackwater, food waste, lignocellulosic waste, as well as co-digested feeds to be considered. Paper 2 presents a framework that uses two-stage kinetic models and stability objectives for digester synthesis (considering inoculum to substrate ratio and instantaneous methanogenic yield) while considering the effects of model structure and sources of inoculum used to startup digester operation. The findings illustrate that the inoculum characteristics influences the structure of the kinetic model used to describe the growth of anaerobic microorganisms and hence the performance targets and digester configurations obtained. Paper 3 presents a framework that uses one-stage kinetic models and economic design objectives for digester network synthesis (developing economic evaluation models based on known economic feasibility indicators as well as macroeconomic parameters), with industrial wastewater as feedstocks. The results illustrate that synthesis of the anaerobic digesters can be tackled using both technical and economic parameters such as payback period as well as country-specific macroeconomic parameters such as interest rate and renewable energy feedin tariff rate. A change in the value of any of these parameters affects the optimal digester configuration. Paper 4 presents a framework that requires no kinetic model for digester synthesis and couples fuzzy multicriteria decision tools with attainable regions for simultaneous synthesis of digester structures and selection of digester subunits considering both techno-economic and environmental aspects. This implies that for the same digester structure, defined in terms of plug flow and continuous stirred tank reactors, the subunits (mainly type of plug flow digester) will differ based on the practical considerations for operating the digester system. Paper 5 presents one of the more significant findings of the study by introducing a framework that simultaneously analyse model reliability, quantifies uncertainty in model states and construct attainable regions that are self-optimizing. Hence, when using attainable regions for performance targeting and digester network synthesis, the results indicate that it is possible to propagate uncertainty of model prediction onto the attainable regions to obtain self-optimizing attainable regions, which is generally smaller than the attainable region but has an advantage of increased robustness. Summarily, the study indicates that using digester networks as opposed to single digesters is able to bypass regions of lower reactivity and improve performance of the anaerobic treatment process. The decision support system should be considered the first point of contact, and used to compliment experiments during planning, design, scale-up and installation of anaerobic digestion plants involving multistage digesters. This will significantly reduce the number of expensive prototype systems and time-consuming studies usually required to obtain an optimal configuration of anaerobic digesters. It is also worth mentioning that even though the study is based on the anaerobic treatment process, the developed frameworks can be applied for synthesis and optimization of other biochemical processes

Attainable regions and fuzzy multi-criteria decisions: Modeling a novel configuration of methane bioreactor using experimental limits of operation

This study sets out to develop an approach that couples attainable regions and fuzzy multicriteria decision methods for modeling optimal configurations of multistage digesters without using a kinetic model of the process. The approach is based on geometric analysis of methane curves as their shapes contain valuable insight into substrate biodegradability characteristics during anaerobic digestion. With the case study of abattoir waste, the results indicate that the optimal batch operation policy involves four anaerobic sequencing batch reactors operated in series with fresh feed being added at the second and the four stages (fed-batch systems). For continuous mode operation, the optimal configuration involves a continuous stirred tank digester with bypass from feed followed by an anaerobic baffled digester, which has been used to obtain a novel prototype. The methodological framework presented in this study can be adopted to enhance design of multistage anaerobic digesters especially when reliable kinetic models are unavailable

Biodigester Rapid Analysis and Design System (B-RADeS): A candidate attainable region-based simulator for the synthesis of biogas reactor structures

Anaerobic digesters are seldom designed based on process kinetics, but rather on a combination of hydraulic and organic loading, which may limit operational performance. This study focuses on the incorporation of process kinetics in the design of anaerobic digesters, within the attainable region conceptual framework. Candidate attainable regions for anaerobic digesters are identified using the software environment Biodigester Rapid Analysis and Design System (B-RADeS), which couples, biodegradation kinetics as well as economic parameters for the synthesis of biodigester structures. By considering swine, palm oil and pharmaceutical wastewaters, payback periods of 0.5, 1 & 2years, and substrate, kinetic model and/or economic parameters, a promising digester structure (and associated hydraulic retention times) is synthesized, consisting of a CSTR followed by PFR (15days), CSTR (4.8hours) and a PFR with bypass of feed (3days). The framework offers great promise for widespread practical application.publishedVersio

A coupled modeling of design and investment parameters for optimal operation of methane bioreactors: Attainable region concept approach

Current practice to design methane bioreactors does not consider all degrees of freedom simultaneously, which raises question of global optimality. This study presents a model-based design framework, which simultaneously integrates process kinetics and business parameters into the design process, a key motivation for investors. Within the study, a methane bioreactor model is presented and kinetic models incorporating different economic feasibility indicators (PBP and BCR) are developed. The methane bioreactor model gives a good prediction of test data for digestion of diary manure and the natural patterns of payback period and benefit cost ratio are predicted. Stochastic stimulation is presented to include robustness in the design process and overall yield coefficients are illustrated for model dimensionality reduction. Two-dimensional attainable region is introduced as a reliable technique for defining limits of achievability as well as obtaining optimal methane bioreactor structures. Finally, a schematic model of the design process is established.publishedVersio

Self-optimizing attainable regions of the anaerobic treatment process: Modelling performance targets under kinetic uncertainty

Despite the advantage of model-based design, anaerobic digesters are seldom designed using biokinetic models due to lack of reliable kinetic coefficients and/or systematic approaches for incorporating kinetic models into digester design. This study presents a systematic framework, which couples practical identifiability, uncertainty quantification and attainable region (AR) concepts for defining process performance targets, especially when reliable kinetic coefficients are unavailable. Within the framework, we introduce the concept of self-optimizing ARs, which define performance targets that results in near optimal operation in spite of variations in kinetic coefficients. Using the case of modified Hill model, only 3 out of the 6 model parameters (unidentifiable set) are responsible for the model prediction uncertainty. The uncertainty bands (mean, 10th percentile and 90th percentile) on the model states has been computed using the Monte Carlo Simulation procedure and attainable regions for the different levels of uncertainty has been constructed and the boundaries interpreted into digester structures. The self-optimizing attainable regions have been defined as the intersection region of the attainable regions corresponding to the mean, 10th percentile and 90th percentile. Incorporating uncertainty significantly reduces performance targets of the process but increases self-optimality in defining performance targets. Unlike the attainable region, which represents the limits of achievability for defined kinetics, the self-optimizing attainable region represents the set of all possible states attainable by the system even in cases of kinetic uncertainty. In summary, the concept of self-optimizing ARs provides a systematic way of defining process performance targets and making economic decisions under conditions of uncertainty

A novel simulation model, BK_BiogaSim for design of onsite anaerobic digesters using two-stage biochemical kinetics: Codigestion of blackwater and organic waste

The design of biogas reactors for blackwater treatment provides special challenges due to significant variability in blackwater characteristics, the complexity of biological systems, and the need, in many cases, to operate in an extremely hygienic environment. In this study, mathematical models were formulated based on microbial growth kinetics to analyze the anaerobic codigestion of blackwater with kitchen waste as well as compare different substrate mixing ratios. The modelling approach used has the advantage of simulating the process with very little input data and eliminates the need to quantify the viable bacteria biomass, which is usually very difficult to estimate during anaerobic digestion. The validity of models is assessed by using a new statistical coefficient (α), which cumulates the effect of four known parameters: coefficient of determination (R2), adjusted coefficient of determination (R2Adj), reduced chi-square (χ2) and root mean square error (RMSE). A numerical calculation interface was designed to quickly and cheaply simulate different digestion scenarios, display results to user and evaluate the effect of input variation on the system's dynamics. Three simulation cases studies were considered each with different mixing ratios of black water to kitchen waste: Case 1 (50:50), Case 2 (25:75) and Case 3 (pure kitchen waste). The, Moser and Andrew based models were most appropriate in describing biogas kinetics for case study 1 (α-values of 0.2238 and 0.2596 respectively), the Monod and Moser based models most appropriate for case 2 (α-values of 0.0987 and 0.1266 respectively), while the Bergter and Haldane based models were most appropriate for case 3 (α-value of 0.0348 and 0.0347 respectively). The results of this study can be used to facilitate design and optimization of biogas sanitation units treating blackwater and kitchen waste

A coupled modeling of design and investment parameters for optimal operation of methane bioreactors: Attainable region concept approach

Current practice to design methane bioreactors does not consider all degrees of freedom simultaneously, which raises question of global optimality. This study presents a model-based design framework, which simultaneously integrates process kinetics and business parameters into the design process, a key motivation for investors. Within the study, a methane bioreactor model is presented and kinetic models incorporating different economic feasibility indicators (PBP and BCR) are developed. The methane bioreactor model gives a good prediction of test data for digestion of diary manure and the natural patterns of payback period and benefit cost ratio are predicted. Stochastic stimulation is presented to include robustness in the design process and overall yield coefficients are illustrated for model dimensionality reduction. Two-dimensional attainable region is introduced as a reliable technique for defining limits of achievability as well as obtaining optimal methane bioreactor structures. Finally, a schematic model of the design process is established

Use of attainable regions for synthesis and optimization of multistage anaerobic digesters

Anaerobic digestion involves multiple reactions, and when operated as a single stage, the process conditions are only suitable for all the reactions with no particular reaction being optimized, hence limiting overall performance. Multistage anaerobic digestion, in which multiple digesters are operated in a network are designed to optimize each process reaction, but very few writers have drawn on any systematic procedure for the design of digester networks. This study is about multistage digester networks, but contrary to traditional multistage digestion articles that focus on the experimental evaluation of a predefined network configuration, this study develops a systematic methodological framework based on the concept of attainable regions for optimal synthesis of digester networks. Within the framework, a simplified model is developed, which accounts for the geometric characteristics of fundamental anaerobic digester types. The model is validated with experimental data of diary, horse, goat, chicken and swine manure, and shows good agreement (model errors between 0.01 and 0.06). The attainable regions and their optimized parameters differ for each digested substrate, and the optimal networks are made of different combinations of digesters operated in a continuous (axial mixing) and/or plug flow (no axial mixing) mode. This substrate effect on attainable regions shows great promises as it paves the way for other substrates such as food waste, lignocellulosic waste, co-digested feeds, etc. This study though preliminary presents a breakthrough in extending the use of digester networks to solve more operational challenges as well as support retrofitting multi-stage systems into facilities where single-stage digesters already exist