6 research outputs found
Toward a Computer-Aided Synthesis and Design of Biorefinery Networks: Data Collection and Management Using a Generic Modeling Approach
Recent
research into biorefineries resulted in many competing concepts
and technologies for conversion of renewable biobased feedstock into
an array of promising products including fuels, chemicals, materials,
etc. The topic of this study is collection and management of the complex
biorefinery data that are needed among others to support the superstructure-based
optimization studies. To this end, we first formulate an integrated
thermochemical and biochemical biorefinery superstructure and then
use a generic modeling approach to represent each processing technology
in the superstructure. The generic model parameters includes reaction
yield, utility consumption, and separation efficiency among others,
which are identified on the basis of input–output data (generated
from rigorous models) collected from detailed biorefinery case studies
reported in the open literature. The outcome is a verified database
for the extended biorefinery networks combining thermochemical and
biochemical platforms that represents 2882 potential biorefinery routes.
The validated biorefinery database is made public and can be used
to cross-validate and benchmark new biorefinery technologies and concepts
as well as in superstructure-based optimization studies
Dynamic Plantwide Modeling, Uncertainty, and Sensitivity Analysis of a Pharmaceutical Upstream Synthesis: Ibuprofen Case Study
A dynamic plantwide
model was developed for the synthesis of the
active pharmaceutical ingredient (API) ibuprofen, following the Hoescht
synthesis process. The kinetic parameters, reagents, products, and
byproducts of the different reactions were adapted from literature,
and the different process operations were integrated until the end
process, crystallization, and isolation of the ibuprofen crystals.
The dynamic model simulations were validated against available measurements
from literature and then used as an enabling tool to analyze the robustness
of design space. To this end, the sensitivity of the design space
toward input disturbances and process uncertainties (from physical
and model parameters) is studied using Monte Carlo simulations. The
results quantify the uncertainty of the quality of product attributes,
with particular focus on crystal size distribution and ibuprofen crystallization.
The ranking of the most influential parameters on the chosen quality
attributes is presented, with crystal growth and water concentration
being the most influential ones. The total amount of saturated solvent,
which propagates from upstream processes, has been shown to highly
influence the total mass of crystal produced and the underspecified
API as well. This dynamic plantwide modeling coupled with Monte Carlo
simulations is valuable to improve the design and optimization of
pharmaceutical processes at early stages, especially to bottleneck
the design space against a range of uncertainties and disturbances
Supply Chain Optimization of Integrated Glycerol Biorefinery: <i>GlyThink</i> Model Development and Application
To further advance the development
and implementation of glycerol-based
biorefinery concepts, it is critical to analyze the glycerol conversion
into high value-added products in a holistic manner, considering both
production as well as the logistics aspects related to the supply
chain structure. To address the optimal design and planning of the
glycerol-based biorefinery supply chain, in this work, we propose
a multiperiod, multistage, and multiproduct Mixed Integer Linear Programming
optimization model, called <i>GlyThink</i>, based upon the
maximization of the net present value (NPV). The proposed model is
able to identify operational decisions, including locations, capacity
levels, technologies, and product portfolio, as well as strategic
decisions such as inventory levels, production amounts, and transportation
to the final markets. Several technologies are considered for the
glycerol valorization to high value-added products. Existing countries
with major production and consumption of biodiesel in Europe are considered
as candidates for the facility sites and demand markets, and their
spatial distribution is also carefully studied. The results showed
that (i) the optimal solution that provides the best NPV is obtained
by establishing a multiplant supply chain for the glycerol-based integrated
biorefinery, built upon four plant site locations (Germany, France,
The Netherlands, and Italy); (ii) if a single-plant alternative is
to be selected, Germany stands out as potentially the best location
for the integrated biorefinery; (iii) government incentives might
play a decisive role in the growth of a glycerol-based economy showing
improved economic feasibility; and, last, (iv) the optimal product
portfolio suggested is based on the production of succinic acid and
lactic acid, followed by epichlorohydrin and poly-3-hydroxybutyrate
(PHB)
Industrial Process Water Treatment and Reuse: A Framework for Synthesis and Design
Mathematical
optimization has shown the potential to contribute
to industrial water management, through the development of the solution
methods needed for optimization-based design of wastewater treatment
and reuse networks (also called water networks). Nevertheless, the
application of this approach is still limited to motivating examples
lacking the ability to handle problems with complexity of industrial
relevance. To address this challenge, in this contribution, we focus
on the integration of wastewater engineering concepts and models,
together with optimization methods and solution algorithms. To this
end, we propose a computer-aided framework for the design of water
treatment and reuse networks. In the framework, optimization methods,
problem analysis tools and wastewater engineering knowledge are integrated
in a computer-aided environment, in order to facilitate the formulation
and solution of the design problems with fair complexity representative
of industrial applications. The framework is demonstrated through
the solution of a case study dealing with the treatment and reuse
of water effluent produced by an oil refinery. The problem is solved,
and a win–win solution is identified, allowing a reduced water
footprint, and the treatment costs are identified
Keynes' "How to pay for the war"
A systematic
methodology to critically assess and screen among
early stage design alternatives was developed for the use of glycerol.
Through deterministic sensitivity analysis it was found that variations
in the product and feedstock prices, total production cost, fixed
capital investment, and discount rate, among others, have high impact
on the project’s profitability analysis. Therefore, the profitability
was tested under uncertainties by using NPV and MSP as economic metrics.
The robust ranking of solutions is presented with respect to minimum
economic risk of the project being nonprofitable (failure to achieve
a positive NPV times the consequential profit loss). It was found
that the best potential options for glycerol valorization is through
the the production of either (i) lactic acid (9 MM economic risk with 76% probability of failure
to achieve a positive NPV); or finally, (iii) 1,2-propanediol (16
MM$ economic risk with 68% probability of failure to achieve a positive
NPV). As a risk reduction strategy, a multiproduct biorefinery is
suggested which is capable of switching between the production of
lactic acid and succinic acid. This solution comes with increased
capital investment; however, it leads to more robust NPV and decreased
economic risk by approximately 20%, therefore creating a production
plant that can continuously adapt to market forces and thus optimize
profitability
Estimation of Environment-Related Properties of Chemicals for Design of Sustainable Processes: Development of Group-Contribution<sup>+</sup> (GC<sup>+</sup>) Property Models and Uncertainty Analysis
The aim of this work is to develop group-contribution<sup>+</sup> (GC<sup>+</sup>) method (combined group-contribution (GC)
method
and atom connectivity index (CI) method) based property models to
provide reliable estimations of environment-related properties of
organic chemicals together with uncertainties of estimated property
values. For this purpose, a systematic methodology for property modeling
and uncertainty analysis is used. The methodology includes a parameter
estimation step to determine parameters of property models and an
uncertainty analysis step to establish statistical information about
the quality of parameter estimation, such as the parameter covariance,
the standard errors in predicted properties, and the confidence intervals.
For parameter estimation, large data sets of experimentally measured
property values of a wide range of chemicals (hydrocarbons, oxygenated
chemicals, nitrogenated chemicals, poly functional chemicals, etc.)
taken from the database of the US Environmental Protection Agency
(EPA) and from the database of USEtox is used. For property modeling
and uncertainty analysis, the Marrero and Gani GC method and atom
connectivity index method have been considered. In total, 22 environment-related
properties, which include the fathead minnow 96-h LC<sub>50</sub>, Daphnia magna 48-h LC<sub>50</sub>, oral rat LD<sub>50</sub>, aqueous solubility, bioconcentration factor, permissible
exposure limit (OSHA-TWA), photochemical oxidation potential, global
warming potential, ozone depletion potential, acidification potential,
emission to urban air (carcinogenic and noncarcinogenic), emission
to continental rural air (carcinogenic and noncarcinogenic), emission
to continental fresh water (carcinogenic and noncarcinogenic), emission
to continental seawater (carcinogenic and noncarcinogenic), emission
to continental natural soil (carcinogenic and noncarcinogenic), and
emission to continental agricultural soil (carcinogenic and noncarcinogenic)
have been modeled and analyzed. The application of the developed property
models for the estimation of environment-related properties and uncertainties
of the estimated property values is highlighted through an illustrative
example. The developed property models provide reliable estimates
of environment-related properties needed to perform process synthesis,
design, and analysis of sustainable chemical processes and allow one
to evaluate the effect of uncertainties of estimated property values
on the calculated performance of processes giving useful insights
into quality and reliability of the design of sustainable processes