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Processes, Volume 5, Issue 1 (March 2017)

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Editorial

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Open AccessEditorial Acknowledgement to Reviewers of Processes in 2016
Processes 2017, 5(1), 4; doi:10.3390/pr5010004
Received: 11 January 2017 / Revised: 11 January 2017 / Accepted: 11 January 2017 / Published: 11 January 2017
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Abstract The editors of Processes would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...] Full article

Research

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Open AccessArticle Modeling Biofilms: From Genes to Communities
Processes 2017, 5(1), 5; doi:10.3390/pr5010005
Received: 8 October 2016 / Revised: 5 January 2017 / Accepted: 17 January 2017 / Published: 23 January 2017
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Abstract
Biofilms are spatially-structured communities of different microbes, which have a huge impact on both ecosystems and human life. Mathematical models are powerful tools for understanding the function and evolution of biofilms as diverse communities. In this article, we give a review of some
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Biofilms are spatially-structured communities of different microbes, which have a huge impact on both ecosystems and human life. Mathematical models are powerful tools for understanding the function and evolution of biofilms as diverse communities. In this article, we give a review of some recently-developed models focusing on the interactions of different species within a biofilm, the evolution of biofilm due to genetic and environmental causes and factors that affect the structure of a biofilm. Full article
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Open AccessArticle Characterization of Whey Protein Oil-In-Water Emulsions with Different Oil Concentrations Stabilized by Ultra-High Pressure Homogenization
Processes 2017, 5(1), 6; doi:10.3390/pr5010006
Received: 1 October 2016 / Revised: 26 January 2017 / Accepted: 30 January 2017 / Published: 10 February 2017
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Abstract
In this study, the effect of ultra-high-pressure homogenization (UHPH: 100 or 200 MPa at 25 °C), in comparison to colloid mill (CM: 5000 rpm at 20 °C) and conventional homogenization (CH: 15 MPa at 60 °C), on the stability of oil-in-water emulsions with
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In this study, the effect of ultra-high-pressure homogenization (UHPH: 100 or 200 MPa at 25 °C), in comparison to colloid mill (CM: 5000 rpm at 20 °C) and conventional homogenization (CH: 15 MPa at 60 °C), on the stability of oil-in-water emulsions with different oil concentrations (10, 30 or 50 g/100 g) emulsified by whey protein isolate (4 g/100 g) was investigated. Emulsions were characterized for their microstructure, rheological properties, surface protein concentration (SPC), stability to creaming and oxidative stability under light (2000 lux/m2). UHPH produced emulsions containing lipid droplets in the sub-micron range (100–200 nm) and with low protein concentrations on droplet surfaces. Droplet size (d3.2, µm) was increased in CH and UHPH emulsions by increasing the oil concentration. CM emulsions exhibited Newtonian flow behaviour at all oil concentrations studied; however, the rheological behaviour of CH and UHPH emulsions varied from Newtonian flow (n ≈ 1) to shear-thinning (n ˂ 1) and thixotropic behaviour in emulsions containing 50% oil. This was confirmed by the non-significant differences in the d4.3 (µm) value between the top and bottom of emulsions in tubes left at room temperature for nine days and also by a low migration velocity measured with a Turbiscan LAB instrument. UHPH emulsions showed significantly lower oxidation rates during 10 days storage in comparison to CM and CH emulsions as confirmed by hydroperoxides and thiobarbituric acid-reactive substances (TBARS). UHPH emulsions treated at 100 MPa were less oxidized than those treated at 200 MPa. The results from this study suggest that UHPH treatment generates emulsions that have a higher stability to creaming and lipid oxidation compared to colloid mill and conventional treatments. Full article
(This article belongs to the Special Issue Emulsification Processes)
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Open AccessArticle A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
Processes 2017, 5(1), 10; doi:10.3390/pr5010010
Received: 19 November 2016 / Revised: 20 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
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Abstract
Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To
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Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning) as well as minimizing a given objective (performing). We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling) independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment) or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example. Full article
(This article belongs to the Special Issue Real-Time Optimization)
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Open AccessArticle An Analysis of the Directional-Modifier Adaptation Algorithm Based on Optimal Experimental Design
Processes 2017, 5(1), 1; doi:10.3390/pr5010001
Received: 1 November 2016 / Revised: 28 November 2016 / Accepted: 15 December 2016 / Published: 22 December 2016
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Abstract
The modifier approach has been extensively explored and offers a theoretically-sound and practically-useful method to deploy real-time optimization. The recent directional-modifier adaptation algorithm offers a heuristic to tackle the modifier approach. The directional-modifier adaptation algorithm, supported by strong theoretical properties and the ease
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The modifier approach has been extensively explored and offers a theoretically-sound and practically-useful method to deploy real-time optimization. The recent directional-modifier adaptation algorithm offers a heuristic to tackle the modifier approach. The directional-modifier adaptation algorithm, supported by strong theoretical properties and the ease of deployment in practice, proposes a meaningful compromise between process optimality and quickly improving the quality of the estimation of the gradient of the process cost function. This paper proposes a novel view of the directional-modifier adaptation algorithm, as an approximation of the optimal trade-off between the underlying experimental design problem and the process optimization problem. It moreover suggests a minor modification in the tuning of the algorithm, so as to make it a more genuine approximation. Full article
(This article belongs to the Special Issue Real-Time Optimization)
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Open AccessFeature PaperArticle Integration of RTO and MPC in the Hydrogen Network of a Petrol Refinery
Processes 2017, 5(1), 3; doi:10.3390/pr5010003
Received: 13 November 2016 / Revised: 28 December 2016 / Accepted: 29 December 2016 / Published: 7 January 2017
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Abstract
This paper discusses the problems associated with the implementation of Real Time Optimization/Model Predictive Control (RTO/MPC) systems, taking as reference the hydrogen distribution network of an oil refinery involving eighteen plants. This paper addresses the main problems related to the operation of the
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This paper discusses the problems associated with the implementation of Real Time Optimization/Model Predictive Control (RTO/MPC) systems, taking as reference the hydrogen distribution network of an oil refinery involving eighteen plants. This paper addresses the main problems related to the operation of the network, combining data reconciliation and a RTO system, designed for the optimal generation and redistribution of hydrogen, with a predictive controller for the on-line implementation of the optimal policies. This paper describes the architecture of the implementation, showing how RTO and MPC can be integrated, as well as the benefits obtained in terms of improved information about the process, increased hydrocarbon load to the treatment plants and reduction of the hydrogen required for performing the operations. Full article
(This article belongs to the Special Issue Real-Time Optimization)
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Open AccessFeature PaperArticle Poly(methacrylic acid-ran-2-vinylpyridine) Statistical Copolymer and Derived Dual pH-Temperature Responsive Block Copolymers by Nitroxide-Mediated Polymerization
Processes 2017, 5(1), 7; doi:10.3390/pr5010007
Received: 19 January 2017 / Revised: 8 February 2017 / Accepted: 13 February 2017 / Published: 21 February 2017
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Abstract
Nitroxide-mediated polymerization using the succinimidyl ester functional unimolecular alkoxyamine initiator (NHS-BlocBuilder) was used to first copolymerize tert-butyl methacrylate/2-vinylpyridine (tBMA/2VP) with low dispersity (Đ = 1.30–1.41) and controlled growth (linear number average molecular Mn versus conversion, Mn =
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Nitroxide-mediated polymerization using the succinimidyl ester functional unimolecular alkoxyamine initiator (NHS-BlocBuilder) was used to first copolymerize tert-butyl methacrylate/2-vinylpyridine (tBMA/2VP) with low dispersity (Đ = 1.30–1.41) and controlled growth (linear number average molecular Mn versus conversion, Mn = 3.8–10.4 kg·mol−1) across a wide composition of ranges (initial mol fraction 2VP, f2VP,0 = 0.10–0.90). The resulting statistical copolymers were first de-protected to give statistical polyampholytic copolymers comprised of methacrylic acid/2VP (MAA/2VP) units. These copolymers exhibited tunable water-solubility due to the different pKas of the acidic MAA and basic 2VP units; being soluble at very low pH < 3 and high pH > 8. One of the tBMA/2VP copolymers was used as a macroinitiator for a 4-acryloylmorpholine/4-acryloylpiperidine (4AM/4AP) mixture, to provide a second block with thermo-responsive behavior with tunable cloud point temperature (CPT), depending on the ratio of 4AM:4AP. Dynamic light scattering of the block copolymer at various pHs (3, 7 and 10) as a function of temperature indicated a rapid increase in particle size >2000 nm at 22–27 °C, corresponding to the 4AM/4AP segment’s thermos-responsiveness followed by a leveling in particle size to about 500 nm at higher temperatures. Full article
(This article belongs to the Special Issue Water Soluble Polymers)
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Open AccessArticle AMPS/AAm/AAc Terpolymerization: Experimental Verification of the EVM Framework for Ternary Reactivity Ratio Estimation
Processes 2017, 5(1), 9; doi:10.3390/pr5010009
Received: 5 January 2017 / Revised: 17 February 2017 / Accepted: 21 February 2017 / Published: 25 February 2017
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Abstract
The complete error-in-variables-model (EVM) framework, consisting of both design of experiments and parameter estimation stages, is applied to the terpolymerization of 2-acrylamido-2-methylpropane sulfonic acid (AMPS, M1), acrylamide (AAm, M2) and acrylic acid (AAc, M3). This water-soluble terpolymer
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The complete error-in-variables-model (EVM) framework, consisting of both design of experiments and parameter estimation stages, is applied to the terpolymerization of 2-acrylamido-2-methylpropane sulfonic acid (AMPS, M1), acrylamide (AAm, M2) and acrylic acid (AAc, M3). This water-soluble terpolymer has potential for applications in enhanced oil recovery, but the associated terpolymerization kinetic characteristics are largely unstudied. In the current paper, EVM is used to design optimal experiments (for the first time in the literature), and reactivity ratios are subsequently estimated based on both low and medium-high conversion data. The results from the medium-high conversion data are more precise than those from the low conversion data, and are therefore used next to predict the terpolymer composition trajectory over the full course of conversion. Good agreement is seen between experimental data and model predictions, which confirms the accuracy of the newly determined ternary reactivity ratios: r12 = 0.66, r21 = 0.82, r13 = 0.82, r31 = 0.61, r23 = 1.61, r32 = 0.25. Full article
(This article belongs to the Special Issue Water Soluble Polymers)
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Open AccessArticle A Modifier-Adaptation Strategy towards Offset-Free Economic MPC
Processes 2017, 5(1), 2; doi:10.3390/pr5010002
Received: 16 November 2016 / Revised: 15 December 2016 / Accepted: 20 December 2016 / Published: 29 December 2016
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Abstract
We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in
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We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem. Full article
(This article belongs to the Special Issue Real-Time Optimization)
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Open AccessArticle Sensitivity-Based Economic NMPC with a Path-Following Approach
Processes 2017, 5(1), 8; doi:10.3390/pr5010008
Received: 26 November 2016 / Revised: 3 February 2017 / Accepted: 13 February 2017 / Published: 27 February 2017
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Abstract
We present a sensitivity-based predictor-corrector path-following algorithm for fast nonlinear model predictive control (NMPC) and demonstrate it on a large case study with an economic cost function. The path-following method is applied within the advanced-step NMPC framework to obtain fast and accurate approximate
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We present a sensitivity-based predictor-corrector path-following algorithm for fast nonlinear model predictive control (NMPC) and demonstrate it on a large case study with an economic cost function. The path-following method is applied within the advanced-step NMPC framework to obtain fast and accurate approximate solutions of the NMPC problem. In our approach, we solve a sequence of quadratic programs to trace the optimal NMPC solution along a parameter change. A distinguishing feature of the path-following algorithm in this paper is that the strongly-active inequality constraints are included as equality constraints in the quadratic programs, while the weakly-active constraints are left as inequalities. This leads to close tracking of the optimal solution. The approach is applied to an economic NMPC case study consisting of a process with a reactor, a distillation column and a recycler. We compare the path-following NMPC solution with an ideal NMPC solution, which is obtained by solving the full nonlinear programming problem. Our simulations show that the proposed algorithm effectively traces the exact solution. Full article
(This article belongs to the Special Issue Real-Time Optimization)
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Open AccessArticle Photorespiration and Rate Synchronization in a Phototroph-Heterotroph Microbial Consortium
Processes 2017, 5(1), 11; doi:10.3390/pr5010011
Received: 25 November 2016 / Revised: 26 January 2017 / Accepted: 15 February 2017 / Published: 2 March 2017
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Abstract
The process of oxygenic photosynthesis is robust and ubiquitous, relying centrally on input of light, carbon dioxide, and water, which in many environments are all abundantly available, and from which are produced, principally, oxygen and reduced organic carbon. However, photosynthetic machinery can be
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The process of oxygenic photosynthesis is robust and ubiquitous, relying centrally on input of light, carbon dioxide, and water, which in many environments are all abundantly available, and from which are produced, principally, oxygen and reduced organic carbon. However, photosynthetic machinery can be conflicted by the simultaneous presence of carbon dioxide and oxygen through a process sometimes called photorespiration. We present here a model of phototrophy, including competition for RuBisCO binding sites between oxygen and carbon dioxide, in a chemostat-based microbial population. The model connects to the idea of metabolic pathways to track carbon and degree of reduction through the system. We find decomposition of kinetics into elementary flux modes a mathematically natural way to study synchronization of mismatched rates of photon input and chemostat turnover. In the single species case, though total biomass is reduced by photorespiration, protection from excess light exposures and its consequences (oxidative and redox stress) may result. We also find the possibility that a consortium of phototrophs with heterotrophs can recycle photorespiration byproduct into increased biomass at the cost of increase in oxidative product (here, oxygen). Full article
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Open AccessArticle Poly(Poly(Ethylene Glycol) Methyl Ether Methacrylate) Grafted Chitosan for Dye Removal from Water
Processes 2017, 5(1), 12; doi:10.3390/pr5010012
Received: 1 February 2017 / Revised: 3 March 2017 / Accepted: 11 March 2017 / Published: 14 March 2017
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Abstract
As the demand for textile products and synthetic dyes increases with the growing global population, textile dye wastewater is becoming one of the most significant water pollution contributors. Azo dyes represent 70% of dyes used worldwide, and are hence a significant contributor to
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As the demand for textile products and synthetic dyes increases with the growing global population, textile dye wastewater is becoming one of the most significant water pollution contributors. Azo dyes represent 70% of dyes used worldwide, and are hence a significant contributor to textile waste. In this work, the removal of a reactive azo dye (Reactive Orange 16) from water by adsorption with chitosan grafted poly(poly(ethylene glycol) methyl ether methacrylate) (CTS-GMA-g-PPEGMA) was investigated. The chitosan (CTS) was first functionalized with glycidyl methacrylate and then grafted with poly(poly(ethylene glycol) methyl ether methacrylate) using a nitroxide-mediated polymerization grafting to approach. Equilibrium adsorption experiments were carried out at different initial dye concentrations and were successfully fitted to the Langmuir and Freundlich adsorption isotherm models. Adsorption isotherms showed maximum adsorption capacities of CTS-g-GMA-PPEGMA and chitosan of 200 mg/g and 150 mg/g, respectively, while the Langmuir equations estimated 232 mg/g and 194 mg/g, respectively. The fundamental assumptions underlying the Langmuir model may not be applicable for azo dye adsorption, which could explain the difference. The Freundlich isotherm parameters, n and K, were determined to be 2.18 and 17.7 for CTS-g-GMA-PPEGMA and 0.14 and 2.11 for chitosan, respectively. An “n” value between one and ten generally indicates favorable adsorption. The adsorption capacities of a chitosan-PPEGMA 50/50 physical mixture and pure PPEGMA were also investigated, and both exhibited significantly lower adsorption capacities than pure chitosan. In this work, CTS-g-GMA-PPEGMA proved to be more effective than its parent chitosan, with a 33% increase in adsorption capacity. Full article
(This article belongs to the Special Issue Water Soluble Polymers)
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Open AccessFeature PaperArticle Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome
Processes 2017, 5(1), 13; doi:10.3390/pr5010013
Received: 6 February 2017 / Revised: 13 March 2017 / Accepted: 15 March 2017 / Published: 18 March 2017
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Abstract
The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut
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The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut microbiome has been widely studied, relatively little is known about the role of interspecies interactions in promoting microbiome stability and function. We developed a biofilm metabolic model of a very simple gut microbiome community consisting of a representative bacteroidete (Bacteroides thetaiotaomicron), firmicute (Faecalibacterium prausnitzii) and proteobacterium (Escherichia coli) to investigate the putative role of metabolic byproduct cross feeding between species on community stability, robustness and flexibility. The model predicted coexistence of the three species only if four essential cross-feeding relationships were present. We found that cross feeding allowed coexistence to be robustly maintained for large variations in biofilm thickness and nutrient levels. However, the model predicted that community composition and short chain fatty acid levels could be strongly affected only over small ranges of byproduct uptake rates, indicating a possible lack of flexibility in our cross-feeding mechanism. Our model predictions provide new insights into the impact of byproduct cross feeding and yield experimentally testable hypotheses about gut microbiome community stability. Full article
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