Processes2016, 4(3), 23; doi:10.3390/pr4030023 - published 25 July 2016 Show/Hide Abstract
Abstract: This contribution describes a novel process systems engineering framework that couples advanced control with sustainability evaluation for the optimization of process operations to minimize environmental impacts associated with products, materials and energy. The implemented control strategy combines a biologically-inspired method with optimal control concepts for finding more sustainable operating trajectories. The sustainability assessment of process operating points is carried out by using the U.S. EPA’s Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) tool that provides scores for the selected indicators in the economic, material efficiency, environmental and energy areas. The indicator scores describe process performance on a sustainability measurement scale, effectively determining which operating point is more sustainable if there are more than several steady states for one specific product manufacturing. Through comparisons between a representative benchmark and the optimal steady states obtained through the implementation of the proposed controller, a systematic decision can be made in terms of whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous fermentation process for fuel production, whose material and energy time variation models are characterized by multiple steady states and oscillatory conditions.
Processes2016, 4(3), 22; doi:10.3390/pr4030022 - published 22 July 2016 Show/Hide Abstract
Abstract: Despite continuous research effort, patients with type 1 diabetes mellitus (T1D) experience difficulties in daily adjustments of their blood glucose concentrations. New technological developments in the form of implanted intravenous infusion pumps and continuous blood glucose sensors might alleviate obstacles for the automatic adjustment of blood glucose concentration. These obstacles consist, for example, of large time-delays and insulin storage effects for the subcutaneous/interstitial route. Towards the goal of an artificial pancreas, we present a novel feedback controller approach that combines classical loop-shaping techniques with gain-scheduling and modern -robust control approaches. A disturbance rejection design is proposed in discrete frequency domain based on the detailed model of the diabetic Göttingen minipig. The model is trimmed and linearised over a large operating range of blood glucose concentrations and insulin sensitivity values. Controller parameters are determined for each of these operating points. A discrete loop-shaping compensator is designed to increase robustness of the artificial pancreas against general coprime factor uncertainty. The gain scheduled controller uses subcutaneous insulin injection as a control input and determines the controller input error from intravenous blood glucose concentration measurements, where parameter scheduling is achieved by an estimator of the insulin sensitivity parameter. Thus, only one controller stabilises a family of animal models. The controller is validated in silico with a total number of five Göttingen Minipig models, which were previously obtained by experimental identification procedures. Its performance is compared with an experimentally tested switching PI-controller.
Processes2016, 4(3), 21; doi:10.3390/pr4030021 - published 15 July 2016 Show/Hide Abstract
Abstract: There is currently much interest in pomegranate juice because of the high content of phenolic compounds. Moreover, the interest in the separation of bioactive compounds from natural sources has remarkably grown. In this work, for the first time, the Punica granatum L. (pomegranate) juice—clarified by using polyvinylidene fluoride (PVDF) and polysulfone (PSU) hollow fiber (HF) membranes prepared in the laboratory—was screened for its antioxidant properties by using different in vitro assays, namely 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), Ferric Reducing Antioxidant Power (FRAP), and β-carotene bleaching tests, and for its potential inhibitory activity of the carbohydrate-hydrolysing enzymes, α-amylase and α-glucosidase. The effects of clarification on quality characteristics of the juice were also investigated in terms of total phenols, flavonoids, anthocyanins, and ascorbic acid. Experimental results indicated that PVDF membranes presented a lower retention towards healthy phytochemicals in comparison to PSU membranes. Accordingly, the juice clarified with PVDF membranes showed the best antioxidant activity. Moreover, the treatment with PVDF membranes produced a clarified juice with 2.9-times fold higher α-amylase inhibitory activity in comparison to PSU (IC50 value of 75.86 vs. 221.31 μg/mL, respectively). The same trend was observed using an α-glucosidase inhibition test. These results highlight the great potential of the clarified juice as a source of functional constituents.
Processes2016, 4(3), 20; doi:10.3390/pr4030020 - published 30 June 2016 Show/Hide Abstract
Abstract: Representing the uncertainties with a set of scenarios, the optimization problem resulting from a robust nonlinear model predictive control (NMPC) strategy at each sampling instance can be viewed as a large-scale stochastic program. This paper solves these optimization problems using the parallel Schur complement method developed to solve stochastic programs on distributed and shared memory machines. The control strategy is illustrated with a case study of a multidimensional unseeded batch crystallization process. For this application, a robust NMPC based on min–max optimization guarantees satisfaction of all state and input constraints for a set of uncertainty realizations, and also provides better robust performance compared with open-loop optimal control, nominal NMPC, and robust NMPC minimizing the expected performance at each sampling instance. The performance of robust NMPC can be improved by generating optimization scenarios using Bayesian inference. With the efficient parallel solver, the solution time of one optimization problem is reduced from 6.7 min to 0.5 min, allowing for real-time application.
Processes2016, 4(2), 19; doi:10.3390/pr4020019 - published 6 June 2016 Show/Hide Abstract
Abstract: Hot-melt extrusion is commonly applied for forming products, ranging from metals to plastics, rubber and clay composites. It is also increasingly used for the production of pharmaceuticals, such as granules, pellets and tablets. In this context, mathematical modeling plays an important role to determine the best process operating conditions, but also to possibly develop software sensors or controllers. The early models were essentially black-box and relied on the measurement of the residence time distribution. Current models involve mass, energy and momentum balances and consists of (partial) differential equations. This paper presents a literature review of a range of existing models. A common case study is considered to illustrate the predictive capability of the main candidate models, programmed in a simulation environment (e.g., MATLAB). Finally, a comprehensive distributed parameter model capturing the main phenomena is proposed.