Enhanced lipid and biodiesel production from glucose‐fed activated sludge: Kinetics and microbial community analysis

An innovative approach to increase biofuel feedstock lipid yields from municipal sewage sludge via manipulation of carbon‐to‐nitrogen (C:N) ratio and glucose loading in activated sludge bioreactors was investigated. Sludge lipid and fatty acid methyl ester (biodiesel) yields (% cell dry weight, CDW) were enhanced via cultivation in activated sludge bioreactors operated at high initial C:N ratio (≥40:1) and glucose loading (≥40 g L^?1). Under C:N 70, 60 g L^?1 glucose loading, a maximum of 17.5 ± 3.9 and 10.2 ± 2.0% CDW lipid and biodiesel yields, respectively, were achieved after 7 d of cultivation. The cultured sludge lipids contained mostly C₁₆? C₁₈ fatty acids, with oleic acid consistently accounting for 40–50% of the total fatty acids. Microbial composition in activated sludge exposed to C:N 70 shifted toward specific gammaproteobacteria, suggesting their relevance in lipid production in wastewater microbiota and potential value in biofuel synthesis applications. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Large‐scale optimization strategies for pressure swing adsorption cycle synthesis

Pressure swing adsorption (PSA) is an efficient method for gas separation and is a potential candidate for carbon dioxide (CO₂) capture from power plants. However, few PSA cycles have been designed for this purpose; the optimal design and operation of PSA cycles for CO₂ capture, as well as other systems, remains a very challenging task. In this study, we present a systematic optimization‐based formulation for the synthesis and design of novel PSA cycles for CO₂ capture in IGCC power plants, which can simultaneously produce hydrogen (H₂) and CO₂ at high purity and high recovery. Here, we apply a superstructure‐based approach to simultaneously determine optimal cycle configurations and design parameters for PSA units. This approach combines automatic differentiation, efficient ODE solvers for the state and sensitivity equations of the PSA model, and state of the art nonlinear programming solvers. Three optimization models are proposed, and two PSA case studies are considered. The first case study considers a binary separation of H₂ and CO₂ at high purity, where specific energy is minimized, whereas the second case study considers a larger five component separation. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3777–3791, 2012     

Optimal design of large‐scale chemical processes under uncertainty: A ranking‐based approach

An approach for the optimal design of chemical processes in the presence of uncertainty was presented. The key idea in this work is to approximate the process constraint functions and model outputs using Power Series Expansions (PSE)‐based functions. The PSE functions are used to efficiently identify the variability in the process constraint functions and model outputs due to multiple realizations in the uncertain parameters using Monte Carlo (MC) sampling methods. A ranking‐based approach is adopted here where the user can assign priorities or probabilities of satisfaction for the different process constraints and model outputs considered in the analysis. The methodology was tested on a reactor–heat exchanger system and the Tennessee Eastman process. The results show that the present method is computationally attractive since the optimal process design is accomplished in shorter computational times when compared to the use of the MC method applied to the full plant model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3243–3257, 2014     

Raising the Decision‐Making Level to Improve the Enterprise‐Wide Production Flexibility

When market demand significantly changes, as in the ongoing worldwide economic crisis, many production plants are forced to operate far from nominal conditions. In this case, the current plant‐wide optimization of production sites is a myopic approach that could lead to plant inefficiencies and unconventional operation issues, thus, resulting in ineffective prevention of economic losses. A way to tackle low‐demand conditions is to raise the decision‐making process from the plant‐wide (or business‐wide) level to the enterprise‐wide (or corporate) level by assigning a Boolean variable to each production site so as to manage their on/off status. By doing so, certain additional (social) constraints may become relevant. The case of operating industrial gases supply chains is considered. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1588–1598, 2013     

A quadratic approximation‐based algorithm for the solution of multiparametric mixed‐integer nonlinear programming problems

An algorithm for the solution of convex multiparametric mixed‐integer nonlinear programming problems arising in process engineering problems under uncertainty is introduced. The proposed algorithm iterates between a multiparametric nonlinear programming subproblem and a mixed‐integer nonlinear programming subproblem to provide a series of parametric upper and lower bounds. The primal subproblem is formulated by fixing the integer variables and solved through a series of multiparametric quadratic programming (mp‐QP) problems based on quadratic approximations of the objective function, while the deterministic master subproblem is formulated so as to provide feasible integer solutions for the next primal subproblem. To reduce the computational effort when infeasibilities are encountered at the vertices of the critical regions (CRs) generated by the primal subproblem, a simplicial approximation approach is used to obtain CRs that are feasible at each of their vertices. The algorithm terminates when there does not exist an integer solution that is better than the one previously used by the primal problem. Through a series of examples, the proposed algorithm is compared with a multiparametric mixed‐integer outer approximation (mp‐MIOA) algorithm to demonstrate its computational advantages. © 2012 American Institute of Chemical Engineers AIChE J, 59: 483–495, 2013     

Synthesis of augmented biofuel processes using solar energy

A method for synthesizing augmented biofuel processes, which improve biomass carbon conversion to liquid fuel (η_carbon) using supplemental solar energy as heat, H₂, and electricity is presented. For a target η_carbon, our method identifies augmented processes requiring the least solar energy input. A nonconvex mixed integer nonlinear programming model allowing for simultaneous mass, heat, and power integration, is built over a process superstructure and solved using global optimization tools. As a case study, biomass thermochemical conversion via gasification/Fischer–Tropsch synthesis and fast‐hydropyrolysis/hydrodeoxygenation (HDO) is considered. The optimal process configurations can be categorized either as standalone (η_carbon ≤ 54%), augmented using solar heat (54% ≤ η_carbon ≤ 74%), or augmented using solar heat and H₂ (74 ≤ η_carbon ≤ 95%). Importantly, the process H₂ consumption is found to be close to the derived theoretical minimum values. To accommodate for the intermittency of solar heat/H₂, we suggest processes that can operate at low and high η_carbon. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2533–2545, 2014     

Insights into the industrial growth of cyanobacteria from a model of the carbon‐concentrating mechanism

With the advent of modern bioengineering tools, photosynthetic organisms are increasingly being engineered to produce chemicals from CO₂ sources, thereby creating a potential route of sustainable chemical production. Cyanobacteria have evolved a carbon‐concentrating mechanism (CCM) that enables growth at low‐environmental carbon concentrations. However at high‐carbon concentrations these benefits may not outweigh synthesis costs. Here, mass transport and kinetic modeling analyses were performed on two species of cyanobacteria as well as a hypothetical no‐CCM mutant. Modeling results correlated with published experimental data. Three conclusions were drawn from the analysis. Carboxysome geometry was unimportant due to the fast relative rate of diffusion of carbon species. Interspecies variations were largely due to active transporters. The no‐carboxysome cell approaches the wild‐type at 10% CO₂. Therefore, in high CO₂ environments the carboxysome and active bicarbonate transporters provide no benefit and a metabolic advantage could be achieved by eliminating the energy‐intensive CCM proteins. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1269–1277, 2014     

Dynamic real‐time optimization and control of a hybrid energy system

A proactive energy management strategy for a stand‐alone hybrid renewable energy system is presented. The study was motivated by the system built in Lambton College (Sarnia, Ontario, Canada) which includes photovoltaic arrays, wind turbine, battery, electrolyzers, hydrogen storage tanks, and fuel cells. The control architecture consists of two levels of hierarchy: (1) optimal predictive scheduling at the supervisory level and (2) local controllers for each of the system units. A “day‐ahead” approach is followed at the supervisory level and a bidirectional communication between the supervisory, proactive control, and the low‐level control layer is established. The proposed energy management strategy accounts for external (i.e., weather and demand) and internal disturbances. The efficacy of the proposed strategy is demonstrated through case studies. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2546–2556, 2014     

Continuous‐time scheduling formulation for straight pipelines

Pipelines represent the most cost‐effective way of transporting large quantities of refined petroleum products over large distances but can be challenging to operate. In this article, we propose a new mixed‐integer linear programming formulation for scheduling straight pipelines with multiple single and dual purpose nodes. The model allows for simultaneous injections and deliveries, and interacting pumping runs, in which a segment of the pipeline simultaneously receives material from its refinery and upstream segment. In contrast to previous batch centric models, it uses segment dependent coordinates. To make it efficient by design, we rely on generalized disjunctive programming and develop disjunctions for which the convex hull reformulation is simple (roughly the same number of variables and constraints as its big‐M counterpart). Through the solution of a set of test cases from the literature, we show a better utilization of the pipeline capacity that is translated into a lower makespan. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1923–1936, 2017     

Equation‐oriented optimization of process flowsheets with dividing‐wall columns

We present a new modeling approach for dividing‐wall columns (DWCs) that is amenable to equation‐oriented flowsheet simulation and optimization. The material, equilibrium, summation, and heat (MESH) equations describing a DWC are highly coupled and nonlinear, making DWC‐based process flowsheets challenging to simulate. Design optimization poses further challenges, typically requiring integer variables to select the number of column stages. To address these difficulties, we represent DWCs as networks of pseudo‐transient (differential‐algebraic) subunit models. We show that these networks have the same steady‐state solution as the original (algebraic) MESH equations, but present significant numerical benefits. We then embed these models in a previously developed pseudo‐transient flowsheet modeling and optimization framework. We further reformulate the models to require only continuous decision variables when selecting the optimal number of stages during design optimization. To illustrate these concepts, we discuss the DWC‐based intensification of the dimethyl ether process. © 2015 American Institute of Chemical Engineers AIChE J, 62: 704–716, 2016     

Nationwide energy supply chain analysis for hybrid feedstock processes with significant CO2 emissions reduction

Integrating diverse energy sources to produce cost‐competitive fuels requires efficient resource management. An optimization framework is proposed for a nationwide energy supply chain network using hybrid coal, biomass, and natural gas to liquids (CBGTL) facilities, which are individually optimized with simultaneous heat, power, and water integration using 162 distinct combinations of feedstock types, capacities, and carbon conversion levels. The model integrates the upstream and downstream operations of the facilities, incorporating the delivery of feedstocks, fuel products, electricity supply, water, and CO₂ sequestration, with their geographical distributions. Quantitative economic trade‐offs are established between supply chain configurations that (a) replace petroleum‐based fuels by 100%, 75%, and 50% and (b) utilize the current energy infrastructures. Results suggest that cost‐competitive fuels for the US transportation sector can be produced using domestically available coal, natural gas, and sustainably harvested biomass via an optimal network of CBGTL plants with significant GHG emissions reduction from petroleum‐based processes. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Transport and deposition kinetics of polymer‐coated multiwalled carbon nanotubes in packed beds

In this study, a modified filtration equation that accounts for the transport and kinetics of polymer‐coated multiwalled carbon nanotubes in columns packed with crushed Berea sandstone is presented. The columns were saturated with brine solution, in which the salt concentration was varied from 0 to 10 wt%. Experimental results show that the polymer effectively eliminates the effects of salt on particle deposition when the salt concentration is less than or equal to 10 wt%. The calculated cumulative particle recovery is as high as 88.47 ± 0.25%. Results show that, at 10 wt% salt concentration, the proposed equation successfully predicts the experimental behavior, especially at the early stages of the breakthrough, where commonly used models fail. It is argued that the new equation accounts for the dynamic change of single collector efficiency as the deposition process advances. When tested against prior results available in the literature, the proposed model agrees with published data from other investigators. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3774–3783, 2016     

Conceptual design of ammonia‐based energy storage system: System design and time‐invariant performance

Chemicals‐based energy storage is promising for integrating intermittent renewables on the utility scale. High round‐trip efficiency, low cost, and considerable flexibility are desirable. To this end, an ammonia‐based energy storage system is proposed. It utilizes a pressurized reversible solid‐oxide fuel cell for power conversion, coupled with external ammonia synthesis and decomposition processes and a steam power cycle. A coupled refrigeration cycle is utilized to recycle nitrogen completely. Pure oxygen, produced as a side‐product in electrochemical water splitting, is used to drive the fuel cell. A first‐principle process model extended by detailed cost calculation is used for process optimization. In this work, the performance of a 100 MW system under time‐invariant operation is studied. The system can achieve a round‐trip efficiency as high as 72%. The lowest levelized cost of delivered energy is obtained at 0.24 $/kWh, which is comparable to that of pumped hydro and compressed air energy storage systems. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1620–1637, 2017     

A centroid‐based sampling strategy for kriging global modeling and optimization

A new sampling strategy is presented for kriging‐based global modeling. The strategy is used within a kriging/response surface (RSM) algorithm for solving NLP containing black‐box models. Black‐box models describe systems lacking the closed‐form equations necessary for conventional gradient‐based optimization. System optima can be alternatively found by building iteratively updated kriging models, and then refining local solutions using RSM. The application of the new sampling strategy results in accurate global model generation at lower sampling expense relative to a strategy using randomized and heuristic‐based sampling for initial and subsequent model construction, respectively. The new strategy relies on construction of an initial kriging model built using sampling data obtained at the feasible region's convex polytope vertices and centroid. Updated models are constructed using additional sampling information obtained at Delaunay triangulation centroids. The new sampling algorithm is applied within the kriging‐RSM framework to several numerical examples and case studies to demonstrate proof of concept. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Multi‐objective optimization superimposed model‐based process design of an enzymatic hydrolysis process

The concepts of green process engineering and rigorous model‐based approaches have proven to be highly beneficial in process engineering. Although a combination of these two principles thus appears extremely promising, it is not found very commonly in literature. The very high complexity resulting from this combination poses great challenges for the process design and design engineers. Therefore, this work presents an innovative methodology for the model‐based process design with superimposed multi‐objective optimization for an exemplary process. This process for the enzymatic hydrolysis of fatty acid methyl ester combines several aspects of green process engineering and represents an exemplary process with an enzymatic liquid‐liquid‐solid reaction system. The optimization results based on operating and investment costs reveal important insights on the exemplary process and highlight the great advantages of the developed methodology as a profound basis for academic and industrial process design purposes. © 2017 American Institute of Chemical Engineers AIChE J, 63: 1974–1988, 2017     

Multistream heat exchangers: Equation‐oriented modeling and flowsheet optimization

Multistream heat exchangers (MHEXs), typically of the plate‐fin or spiral‐wound type, are a key enabler of heat integration in cryogenic processes. Equation‐oriented modeling of MHEXs for flowsheet optimization purposes is challenging, especially when streams undergo phase transformations. Boolean variables are typically used to capture the effect of phase changes, adding considerable difficulty to solving the flowsheet optimization problem. A novel optimization‐oriented MHEX modeling approach that uses a pseudo‐transient approach to rapidly compute stream temperatures without requiring Boolean variables is presented. The model also computes an approximate required heat exchange area to determine the optimal tradeoff between operating and capital expenses. Subsequently, this model is seamlessly integrated in a previously‐introduced pseudo‐transient process modeling and flowsheet optimization framework. Our developments are illustrated with two optimal design case studies, an MHEX representative of air separation operation and a natural gas liquefaction process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1856–1866, 2015     

Greedy algorithm for scheduling batch plants with sequence‐dependent changeovers

This article presents a new algorithm for scheduling multistage batch plants with a large number of orders and sequence‐dependent changeovers. Such problems are either intractable when solved with full‐space approaches or poor solutions result. We use decomposition on the entire set of orders and derive the complete schedule in several iterations, by inserting a couple of orders at a time. The key idea is to allow for partial rescheduling without altering the main decisions in terms of unit assignments and sequencing (linked to the binary variables) so that the combinatorial complexity is kept at a manageable level. The algorithm has been implemented for three alternative continuous‐time mixed integer linear programing models and tested through the solution of 10 example problems for different decomposition settings. The results show that an industrial‐size scheduling problem with 50 orders, 17 units distributed over six stages can effectively be solved in roughly 6 min of computational time. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Modeling the mass transfers during the elaboration of chitosan‐activated carbon composites for medical applications

Hydrogels composites composed of chitosan and activated carbon were prepared for medical applications using the vapor‐induced phase separation process. Since the gelation process involves mass exchanges between the polymer solution and the air, the kinetics of mass transfer were investigated through experimental and modeling approaches. Among the formulation and process parameters, gravimetric measurements exhibited that mass transfers were mostly controlled by the initial ammonia partial pressure. A nonisotherm mass‐transfer model was developed to predict the nonsolvent and solvent exchange rates, therefore, the water and ammonia concentration profiles within the sample during the process. The numerical results were successively validated with gravimetrical kinetic curves obtained in a chamber where the process parameters were controlled. The model aimed also at predicting the pH moving front along the film thickness. The gelation time could also be predicted for different operating conditions (formulation and process parameters). © 2009 American Institute of Chemical Engineers AIChE J, 2009     

A thermal‐flywheel approach to distributed temperature control in microchannel reactors

Microchannel reactors are a promising route for monetizing distributed natural gas resources. However, intensification and miniaturization represent a significant challenge for reactor control. Focusing on autothermal methane‐steam reforming reactors, a novel microchannel reactor temperature control strategy based on confining a layer of phase‐change material (PCM) between the reactor plates is introduced. Melting‐solidification cycles, which occur with latent heat exchange at constant temperature, allow the PCM layer to act as an energy storage buffer—a “thermal flywheel”—constituting a distributed controller that mitigates temperature excursions caused by fluctuations in feedstock quality. A novel stochastic optimization algorithm for selecting the PCM layer thickness (i.e., distributed controller “tuning”) is introduced. Furthermore, a hierarchical control structure, whereby the PCM layer is complemented by a supervisory controller that addresses persistent disturbances, is proposed. The proposed concepts are illustrated in a comprehensive case study using a detailed two‐dimensional reactor model. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2051–2061, 2013