Processes2015, 3(2), 222-234; doi:10.3390/pr3020222 - published 3 April 2015 Show/Hide Abstract
Abstract: While HZSM-5 catalytic cracking of crop oil toward aromatics have been well documented, this work adds to this body of knowledge with a full acid byproduct analysis that provides improved mass balance closure along with a design of experiment optimization of reaction conditions. Fatty acids are an inevitable byproduct when converting any triglyceride oil, but are most often overlooked; despite the impact fatty acids have on downstream processing. Acid analysis verified that only short chain fatty acids, mainly acetic acid, were present in low quantities when all feed oil was reacted. When relatively high fatty acid amounts were present, these were mainly uncracked C16 and C18 fatty acids. Optimization is a balance of aromatics formation vs. unwanted gas products, coke and residual fatty acids. A design of experiments approach was used to provide insight into where the optimal reaction conditions reside for HZSM-5 facilitated reactions. These conditions can then form the basis for further development into a commercially viable process for the production of renewable aromatics and other byproducts.
Processes2015, 3(1), 204-221; doi:10.3390/pr3010204 - published 20 March 2015 Show/Hide Abstract
Abstract: Fibrous materials are prominent among novel chromatographic supports for the separation and purification of biomolecules. In this work, strong anion exchange, quaternary ammonium (Q) functional fibrous adsorbents were evaluated with regards to their physical and functional characteristics. A column packed with Q fibrous adsorbent illustrated the good column packing efficiency of theoretical plate height (H) values and higher permeability coefficients (>0.9 × 10−7 cm2) than commercial adsorbents. For pulse experiments with acetone and lactoferrin as tracers under nonbinding conditions, the total porosity (for acetone) and the interstitial porosity (for lactoferrin) measured 0.97 and 0.47, respectively. The total ionic capacity of the chemically-functionalized Q fiber was 0.51 mmol/mL. The results indicated that the Q fiber had a static binding capacity of 140 mg/mL and a dynamic binding capacity (DBC) of 76 mg/mL for bovine serum albumin (BSA) and showed a linearly-scalable factor (~110 mL) for a column volume with high capacity and high throughput. Furthermore, this adsorptive material had the ability to bind the high molecular weight protein, thyroglobulin, with a capacity of 6 mg/mL. This work demonstrated the column-packed Q fibrous adsorption system as a potential chromatography support that exhibits high capacity at higher flow rates.
Processes2015, 3(1), 178-203; doi:10.3390/pr3010178 - published 16 March 2015 Show/Hide Abstract
Abstract: In this study, we present a novel modeling approach which combines ordinary differential equation (ODE) modeling with logical rules to simulate an archetype biochemical network, the human coagulation cascade. The model consisted of five differential equations augmented with several logical rules describing regulatory connections between model components, and unmodeled interactions in the network. This formulation was more than an order of magnitude smaller than current coagulation models, because many of the mechanistic details of coagulation were encoded as logical rules. We estimated an ensemble of likely model parameters (N = 20) from in vitro extrinsic coagulation data sets, with and without inhibitors, by minimizing the residual between model simulations and experimental measurements using particle swarm optimization (PSO). Each parameter set in our ensemble corresponded to a unique particle in the PSO. We then validated the model ensemble using thrombin data sets that were not used during training. The ensemble predicted thrombin trajectories for conditions not used for model training, including thrombin generation for normal and hemophilic coagulation in the presence of platelets (a significant unmodeled component). We then used flux analysis to understand how the network operated in a variety of conditions, and global sensitivity analysis to identify which parameters controlled the performance of the network. Taken together, the hybrid approach produced a surprisingly predictive model given its small size, suggesting the proposed framework could also be used to dynamically model other biochemical networks, including intracellular metabolic networks, gene expression programs or potentially even cell free metabolic systems.
Processes2015, 3(1), 161-177; doi:10.3390/pr3010161 - published 11 March 2015 Show/Hide Abstract
Abstract: A system is differentially flat if it is Lie–Bäcklund (L-B) equivalent to a free dynamical system that has dimensions equal to that of the input of the original system. Utilizing this equivalence, the problem of nonlinear model predictive control of a flat system can be reduced to a lower dimensional nonlinear programming problem with respect to the flat outputs. In this work, a novel computational method based on Haar wavelets in the time-domain for solving the resulting nonlinear programming problem is developed to obtain an approximation of the optimal flat output trajectory. The Haar wavelet integral operational matrix is utilized to transform the nonlinear programming problem to a finite dimensional nonlinear optimization problem. The proposed approach makes use of flatness as a structural property of nonlinear systems and the convenient mathematical properties of Haar wavelets to develop an efficient computational algorithm for nonlinear model predictive control of differentially flat systems. Further improvement on computational efficiency is achieved by providing solutions with multiple resolutions (e.g., obtaining high resolution solutions only for the near future, but allowing coarse approximation for the later stage in the prediction horizon).
Processes2015, 3(1), 138-160; doi:10.3390/pr3010138 - published 3 March 2015 Show/Hide Abstract
Abstract: Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultrasensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge to an incorrect structure. While only an initial proof-of-concept, the framework presented here could be an important first step toward genome-scale cell-free kinetic modeling of the biosynthetic capacity of industrially important organisms.
Processes2015, 3(1), 113-137; doi:10.3390/pr3010113 - published 17 February 2015 Show/Hide Abstract
Abstract: Performance assessment and retuning techniques for proportional-integral-derivative (PID) controllers are reviewed in this paper. In particular, we focus on techniques that consider deterministic performance and that use routine operating data (that is, set-point and load disturbance step signals). Simulation and experimental results show that the use of integrals of predefined signals can be effectively employed for the estimation of the process parameters and, therefore, for the comparison of the current controller with a selected benchmark.