Modelling and simulation based assessment in sustainable bioprocess development

Modelling and simulation enhance our insight and understanding of chemical processes and aid in identifying bottlenecks and potential improvements. A simplified simulation package, providing a reasonable estimate of material and energy usage and process emissions is often valuable in very early stages of process development, when temporal and financial limitations do not allow for more detailed estimates. Environmental burdens are an increasing concern in industrial processes and various methodologies and tools have been developed for gathering and analysis of process information to enhance understanding of the process system and inform decision makers. The systems nature of these approaches is aimed at mitigation of environmental burdens through improved technologies, sustainable resource consumption and screening of process alternatives. Ideally, the process design team should bring together these tools in early stages of development when design flexibility is greatest. In the present study, such a simplified approach to bioprocess design is demonstrated using a case study for the large-scale production of citric acid

ECUT (Energy Conversion and Utilization Technologies) program: Biocatalysis Project

Fiscal year 1987 research activities and accomplishments for the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Division are presented. The project's technical activities were organized into three work elements. The Molecular Modeling and Applied Genetics work element includes modeling and simulation studies to verify a dynamic model of the enzyme carboxypeptidase; plasmid stabilization by chromosomal integration; growth and stability characteristics of plasmid-containing cells; and determination of optional production parameters for hyper-production of polyphenol oxidase. The Bioprocess Engineering work element supports efforts in novel bioreactor concepts that are likely to lead to substantially higher levels of reactor productivity, product yields, and lower separation energetics. The Bioprocess Design and Assessment work element attempts to develop procedures (via user-friendly computer software) for assessing the economics and energetics of a given biocatalyst process

A decision support tool for landfill methane generation and gas collection

This study presents a decision support tool (DST) to enhance methane generation at individual landfill sites. To date there is no such tool available to provide landfill decision makers with clear and simplified information to evaluate biochemical processes within a landfill site, to assess performance of gas production and to identify potential remedies to any issues. The current lack in understanding stems from the complexity of the landfill waste degradation process. Two scoring sets for landfill gas production performance are calculated with the tool: (1) methane output score which measures the deviation of the actual methane output rate at each site which the prediction generated by the first order decay model LandGEM; and (2) landfill gas indicators’ score, which measures the deviation of the landfill gas indicators from their ideal ranges for optimal methane generation conditions. Landfill gas indicators include moisture content, temperature, alkalinity, pH, BOD, COD, BOD/COD ratio, ammonia, chloride, iron and zinc. A total landfill gas indicator score is provided using multi-criteria analysis to calculate the sum of weighted scores for each indicator. The weights for each indicator are calculated using an analytical hierarchical process. The tool is tested against five real scenarios for landfill sites in UK with a range of good, average and poor landfill methane generation over a one year period (2012). An interpretation of the results is given for each scenario and recommendations are highlighted for methane output rate enhancement. Results demonstrate how the tool can help landfill managers and operators to enhance their understanding of methane generation at a site-specific level, track landfill methane generation over time, compare and rank sites, and identify problems areas within a landfill site

반회분식 생물공정의 파라미터 추정 계산 효율 향상을 위한 모델 기반 실험계획법

학위논문(박사)--서울대학교 대학원 :공과대학 화학생물공학부,2019. 8. 이종민.Identification of batch dynamical systems is a tricky task because of its complexity and nonlinearity. If the macroscopic structure of a model is available, one can utilize Model-based Design of Experiments (MBDOE) method to facilitate the identification process, more precisely, the parameter estimation. However, a few crucial problems arise in utilizing MBDOE for estimating parameters of batch dynamical systems. First, the whole design depends on the initial estimate of the parameters. Second, the gigantic size of the problem prevents one from obtaining reliable solution in practical amount of time. Third, correlation between the parameters inhibits calculation process of MBDOE. In this thesis, we propose two new schemes of MBDOEs that solve issues of the existing MBDOE schemes. The first MBDOE modifies the existing on-line MBDOE into a form that can be efficiently used in large models, solving initial parameter dependency issue, computation time and sensitivity matrix singularity issue. The second MBDOE improves the existing anti-correlation MBDOE into a form suitable for iterative experiments and causes no numerical instability. Finally, we apply the combined scheme of proposed methodologies to the microalgal bioreactor model to demonstrate its use, as well as study various issues that can occur when the algorithm is applied in actual cases.회분식 및 반회분식 반응기 모델은 매우 복잡하고 비선형성이 크기 때문에, 파라미터 추정이 매우 어렵다. 모델에 대한 구조가 알려져 있는 상태라면, 파라미터 추정을 위해서 모델 기반 실험계획법(MBDOE)를 사용할 수 있다. 하지만 이 MBDOE를 회분식 반응기의 파라미터 추정에 적용할 경우 여러 가지의 치명적인 문제점이 발생하게 된다. 첫 번째, MBDOE의 결과가 초기 파라미터 추정치에 따라 달라진다. 두 번째, 문제 자체의 크기가 너무 커서 한정된 시간 안에 믿을 만한 해를 구하기가 불가능하다. 세 번째, 파라미터들간의 상관성 때문에 수치적으로 안정된 MBDOE 계산을 수행 하는 것이 어렵다. 본 논문에서는 이러한 기존의 MBDOE 기법의 문제점들을 해결하는 두 가지의 새로운 MBDOE 기법을 제안한다. 첫 번째 MBDOE는 기존의 온라인 MBDOE를 그 크기가 큰 모델에도 효율적으로 적용 가능한 형태로 개선하여 초기 파라미터에 대한 의존성 문제, 계산 시간 문제와, sensitivity matrix의 불안정성 문제를 해결한다. 두 번째로 제안한 MBDOE는 기존의 anti-correlation MBDOE을 더 개선시켜서 반복 실험에 적당하고 수치적으로 안정한 형태로 발전시킨다. 마지막으로, 이렇게 제안된 두 가지의 방법론을 반회분식 미세조류 모델의 파라미터 추정 문제에 실제로 적용하여, 알고리즘의 사용 방법을 실제적으로 증명하고, 적용 과정에서 발생할 수 있는 다양한 문제들에 대해 탐구하였다.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Identification of batch processes and experimental designs . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Issues of existing MBDOEs . . . . . . . . . . . . . . 4 1.2.1 Dependence on the initial parameter estimate . 4 1.2.2 Numerical size of the problem . . . . . . . . . 4 1.2.3 Correlation between the parameters . . . . . . 5 1.3 Current approaches to the issues . . . . . . . . . . . . 6 1.3.1 Dependence on the initial parameter estimate . 6 1.3.2 Numerical size of the problem . . . . . . . . . 7 1.3.3 Correlation between the parameters . . . . . . 8 1.4 Scope of the study . . . . . . . . . . . . . . . . . . . 10 1.5 Outline of the thesis . . . . . . . . . . . . . . . . . . 11 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . 12 2.1 Model-based design of experiments (MBDOE) . . . . 12 2.1.1 Basic formulation . . . . . . . . . . . . . . . 12 2.1.2 Issues seen in detail . . . . . . . . . . . . . . 14 2.2 On-line MBDOE . . . . . . . . . . . . . . . . . . . . 21 2.3 Anti-correlation MBDOE . . . . . . . . . . . . . . . 25 3. Parameter subset selective on-line MBDOE . . . . . . 27 3.1 Objective of the methodology . . . . . . . . . . . . . 27 3.2 Theoretical formulation . . . . . . . . . . . . . . . . 28 3.2.1 Parameter subset selection . . . . . . . . . . . 28 3.2.2 Optimal input calculation . . . . . . . . . . . 33 3.2.3 Implementation and parameter re-estimation . 34 3.3 Demonstration . . . . . . . . . . . . . . . . . . . . . 34 3.3.1 Model description and problem settings . . . . 36 3.3.2 Result . . . . . . . . . . . . . . . . . . . . . . 37 3.3.3 Comparison for different number of subset parameters . . . . . . . . . . . . . . . . . . . . 44 3.3.4 Effect of model conditions and hyper-parameters on the performance of the scheme . . . . . . . 48 4. Successive complementary anti-correlation MBDOE . 50 4.1 Objective of the method . . . . . . . . . . . . . . . . 50 4.2 Theoretical formulation . . . . . . . . . . . . . . . . 53 4.2.1 Initial experimental design . . . . . . . . . . 53 4.2.2 Complementary design formulation . . . . . . 53 4.2.3 Iteration and termination . . . . . . . . . . . 58 4.3 Case study . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.1 Model description . . . . . . . . . . . . . . . 59 4.3.2 Solution method . . . . . . . . . . . . . . . . 60 4.3.3 Result . . . . . . . . . . . . . . . . . . . . . 61 4.4 Remarks on the choice of hyper parameters . . . . . 73 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . 75 5. Application to a microalgal fed-batch bioreactor . . . 79 5.1 Necessity of the combined scheme . . . . . . . . . . 79 5.2 Overall scheme of the study . . . . . . . . . . . . . . 82 5.3 Model description . . . . . . . . . . . . . . . . . . . 85 5.4 Parameter subset selective on-line MBDOE . . . . . . 89 5.4.1 Simulation settings . . . . . . . . . . . . . . . 89 5.4.2 Result . . . . . . . . . . . . . . . . . . . . . . 91 5.5 Successive complementary anti-correlation MBDOE . 102 5.5.1 Simulation settings . . . . . . . . . . . . . . . 102 5.5.2 Result . . . . . . . . . . . . . . . . . . . . . . 103 5.6 Comparison to the D-optimal-only case . . . . . . . . 113 5.7 Remarks . . . . . . . . . . . . . . . . . . . . . . . . 117 5.7.1 Choice of the solution method . . . . . . . . . 117Docto

MODEL DEVELOPMENT AND SYSTEM OPTIMIZATION TO MINIMIZE GREENHOUSE GAS EMISSIONS FROM WASTEWATER TREATMENT PLANTS

As greenhouse gas emissions (GHG) reduction has drawn considerable attention, various methods have been established to estimate greenhouse gas emissions from wastewater treatment plants (WWTPs). In order to establish a design and operational strategy for GHG mitigation, accurate estimates are essential. However, the existing approaches (e.g. the IPCC protocol and national greenhouse gas inventories) do not cover emissions from all sources in WWTPs and are not sufficient to predict facility-level emissions. The ultimate goal of this research was to improve the quantification of GHG emissions from WWTPs. This was accomplished by creating a new mathematical model based on an existing activated sludge model. The first part of the research proposed a stepwise methodology using elemental balances in order to derive stoichiometry for state variables used in a mass balance based whole-plant wastewater treatment plant model. The two main advantages of the elemental balance method are the inclusion of carbon dioxide (CO2) into the existing model with no mass loss and ease of tracking elemental pathways. The second part of the research developed an integrated model that includes (1) a direct emission model for onsite emissions from treatment processes and (2) an indirect emission model for offsite emissions caused by plant operation. A sensitivity analysis of the proposed model was conducted to identify key input parameters. An uncertainty analysis was also carried out using a Monte Carlo simulation, which provided an estimate of the potential variability in GHG estimations. Finally, in the third part, the research identified an optimal operational strategy that resulted in minimizing operating costs and GHG emission, while simultaneously treating the wastewater at better levels. To do this, an integrated performance index (IPI) was proposed to combine the three criteria. The IPI was then incorporated into an optimization algorithm. The results obtained in this research demonstrated that the variation of GHG emissions is significant across the range of practical operational conditions. With system optimization, however, WWTPs have the potential to reduce GHG emissions without raising operating costs or reducing effluent quality. Further research should include a mechanistic examination of processes that produce methane (CH4) in the wastewater treatment stream and nitrous oxide (N2O) in the sludge treatment stream

Critical and sustainable fluxes: theory, experiments and applications

Over the last ten years, numerous membrane filtration data have been viewed in the light of the concept of critical flux. This concept, used in a number of different ways often without explicit redefinition, is here clarified both from a theoretical and from an experimental viewpoint. Also, a link is make with the sustainable fluxes. Also covered are the various methods of measurement and the influence of membrane and suspension properties on the critical flux. Over the same period of time, models have been developed to explain the observed behaviour. Those for stable colloidal suspensions are based on the existence of repulsive interactions between soft matter constituents. The assumptions and usefulness of various models are discussed. The concept of a critical concentration for phase transition is introduced into the theoretical discussion. For theoreticians and experimentalist, this and the clarified concept of a small set of critical fluxes will continue to provide a valuable framework. For membrane users dealing with most industrial process streams (mixtures and complex fluid) the concept of a sustainable flux (shown as being derived from critical flux) is of a great utility; above a certain key flux (dependent on hydrodynamics, feed conditions and process time) the rate of fouling is economically and environmentally unsustainable. For many, knowledge of the point below which no major irreversible fouling occurs (the critical flux) in a membrane separation will always be of greatest utility

Landfill Gas To Energy Incentives And Benefits

Municipal solid waste (MSW) management strategies typically include a combination of three approaches, recycling, combustion, and landfill disposal. In the US approximately 54% of the generated MSW was landfilled in 2008, mainly because of its simplicity and cost-effectiveness. However, landfills remain a major concern due to potential landfill gas (LFG) emissions, generated from the chemical and biological processes occurring in the disposed waste. The main components of LFG are methane (50-60%) and carbon dioxide (40-50%). Although LFG poses a threat to the environment, if managed properly it is a valuable energy resource due to the methane content. Currently there are over 550 active LFG to energy (LFGTE) facilities in the US, producing renewable energy from LFG. A major challenge in designing/operating a LFGTE facility is the uncertainty in LFG generation rate predictions. LFG generation rates are currently estimated using models that are dependent upon the waste disposal history, moisture content, cover type, and gas collection system, which are associated with significant uncertainties. The objectives of this research were to: Evaluate various approaches of estimating LFG generation and to quantify the uncertainty of the model outcomes based on case-study analysis, Present a methodology to predict long-term LFGTE potential under various operating practices on a regional scale, and Investigate costs and benefits of emitting vs. collecting LFG emissions with regards to operation strategies and regulations. iii The first-order empirical model appeared to be insensitive to the approach taken in quantifying the model parameters, suggesting that the model may be inadequate to accurately describe LFG generation and collection. The uncertainty values for the model were, in general, at their lowest within five years after waste placement ended. Because of the exponential nature, the uncertainty increased as LFG generation declined to low values decades after the end of waste placement. A methodology was presented to estimate LFGTE potential on a regional scale over a 25-year timeframe with consideration of modeling uncertainties. The methodology was demonstrated for the US state of Florida, and showed that Florida could increase the annual LFGTE production by more than threefold by 2035 through installation of LFGTE facilities at all landfills. Results showed that diverting food waste could significantly reduce fugitive LFG emissions, while having minimal effect on the LFGTE potential. Estimates showed that with enhanced landfill operation and energy production practices, LFGTE power density could be comparable to technologies such as wind, tidal, and geothermal. More aggressive operations must be considered to avoid fugitive LFG emissions, which could significantly affect the economic viability of landfills. With little economic motivation for US landfill owners to voluntarily reduce fugitive emissions, regulations are necessary to increase the cost of emitting GHGs. In light of the recent economic recession, it is not likely that a carbon tax will be established; while a carbon trading program will enforce emission caps and provide a tool to offset some costs and improve emission-reduction systems. Immediate action establishing a iv US carbon trading market with carbon credit pricing and trading supervised by the federal government may be the solution. Costs of achieving high lifetime LFG collection efficiencies are unlikely to be covered with revenues from tipping fee, electricity sales, tax credits, or carbon credit trading. Under scenarios of highly regulated LFG emissions, sustainable landfilling will require research, development, and application of technologies to reduce the marginal abatement cost, including: Diverting rapidly decomposable waste to alternative treatment methods, Reducing fugitive emissions through usage daily/intermediate covers with high oxidation potential, Increasing the lifetime LFG collection efficiency, and Increasing LFG energy value – for instance by producing high-methane gas through biologically altering the LFG generation pathwa

Life-cycle Greenhouse Gas Emissions and Water Footprint of Residential Waste Collection and Management Systems

Three troublesome issues concerning residential curbside collection (RCC) and municipal solid waste (MSW) management systems in the United States motivated this research. First, reliance upon inefficient collection and scheduling procedures negatively affect RCC efficiency, greenhouse gas (GHG) emissions, and cost. Second, the neglected impact of MSW management practices on water resources. Third, the implications of alternative fuels on the environmental and financial performance of waste collection where fuel plays a significant rule. The goal of this study was to select the best RCC program, MSW management practice, and collection fuel. For this study, field data were collected for RCC programs across the State of Florida. The garbage and recyclables generation rates were compared based on garbage collection frequency and use of dual-stream (DS) or single-stream (SS) recyclables collection system. The assessment of the collection programs was evaluated based on GHG emissions, while for the first time, the water footprint (WFP) was calculated for the most commonly used MSW management practices namely landfilling, combustion, and recycling. In comparing alternative collection fuels, two multi-criteria decision analysis (MCDA) tools, TOPSIS and SAW, were used to rank fuel alternatives for the waste collection industry with respect to a multi-level environmental and financial decision matrix. The results showed that SS collection systems exhibited more than a two-fold increase in recyclables generation rates, and a ~2.2-fold greater recycling efficiency compared to DS. The GHG emissions associated with the studied collection programs were estimated to be between 36 and 51 kg CO2eq per metric ton of total household waste (garbage and recyclables), depending on the garbage collection frequency, recyclables collection system (DS or SS) and recyclables compaction. When recyclables offsets were considered, the GHG emissions associated with programs using SS were estimated between -760 and -560, compared to between -270 and -210 kg CO2eq per metric ton of total waste for DS programs. In comparing the WFP of MSW management practices, the results showed that the WFP of waste landfilling can be reduced through implementing bioreactor landfilling. The WFP of electricity generated from waste combustion was less than the electricity from landfill gas. Overall, the WFP of electricity from MSW management practices was drastically less than some renewable energy sources. In comparing the WFP offsets of recyclables, the recycling of renewable commodities, e.g. paper, contributed to the highest WFP offsets compared to other commodities, mainly due to its raw material acquisition high WFPs. This suggests that recycling of renewable goods is the best management practice to reduce the WFP of MSW management. Finally, the MCDA of alternative fuel technologies revealed that diesel is still the best option, followed by hydraulic-hybrid waste collection vehicles (WCVs), then landfill gas (LFG) sourced natural gas, fossil natural gas and biodiesel. The elimination of the fueling station criterion from the financial criteria ranked LFG-sourced natural gas as the best option; suggesting that LFG sourced natural gas is the best alternative to fuel WCV when accessible. In conclusion, field data suggest that RCC system design can significantly impact recyclables generation rate and efficiency, and consequently determine environmental and economic impact of collection systems. The WFP concept was suggested as a method to systematically assess the impact of MSW management practices on water resources. A careful consideration of the WFP of MSW management practices and energy recovered from MSW management facilities is essential for the sustainable appropriation of water resources and development