9Allocation and management of agricultural land is of emergent concern due to land scarcity, diminishing 10 supply of energy and water, and the increasing demand of food globally. To achieve social, economic 11 and environmental goals in a specific agricultural land area, people and society must make decisions 12 subject to the demand and supply of food, energy and water (FEW). Interdependence among these 13 three elements, the Food-Energy-Water Nexus (FEW-N), requires that they be addressed concertedly.14 Despite global efforts on data, models and techniques, studies navigating the multi-faceted FEW-N 15 space, identifying opportunities for synergistic benefits, and exploring interactions and trade-offs in 16 agricultural land use system are still limited. Taking an experimental station in China as a model 17 system, we present the foundations of a systematic engineering framework and quantitative decision-18 making tools for the trade-off analysis and optimization of stressed interconnected FEW-N networks. 19 The framework combines data analytics and mixed-integer nonlinear modeling and optimization meth-20 ods establishing the interdependencies and potentially competing interests among the FEW elements 21 in the system, along with policy, sustainability, and feedback from various stakeholders. A multi-22 objective optimization strategy is followed for the trade-off analysis empowered by the introduction 23 of composite FEW-N metrics as means to facilitate decision-making and compare alternative process 24 and technological options. We found the framework works effectively to balance multiple objectives 25 and benchmark the competitions for systematic decisions. The optimal solutions tend to promote the 26 food production with reduced consumption of water and energy, and have a robust performance with 27 alternative pathways under different climate scenarios. 28 (Efstratios N. Pistikopoulos ), jie.li-2@manchester.ac.uk (Jie Li )Agricultural land is the largest ecosystem to provide food for human (Ellis & Ramankutty, 2008). 32 Agricultural production accounts for ∼30% of the global energy consumption, ∼92% of the human 33 water footprint, and over 20% of global greenhouse gas emissions (Alexandratos et al., 2012; Sims, 34 2011). The Food and Agricultural Organization (FAO) estimates a ∼60% increase of food demand 35 (compared with that of 2005/2007) for feeding 9.7 billion people by 2050, but the contribution of 36 cropland expansion to the increase is expected to reduce from 14% to 10% due to environmental reasons 37 at that time (Alexandratos et al., 2012; Ramankutty et al., 2018). Several countries, particularly in 38 the Near East/North Africa and South Asia, have already reached or are close to the limits of land 39 resource (FAO, 2009). Thus, there is an increasing pressure to meet the food demand of current and 40 future human populations with limited land expansion while minimizing the consumption of energy 41and water and conserving the environment. 42Typically, agricultural food production is a w...

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The multicolumn countercurrent solvent gradient purification process (MCSGP) is a semicontinuous, chromatographic separation process used in the production of monoclonal antibodies) . The process is characterized by high model complexity and periodicity that challenge the development of control strategies, necessary for feasible and efficient operation and essential toward continuous production. A novel approach for the development of control policies for the MCSGP process, which enables efficient continuous process control is presented. Based on a high fidelity model, the recently presented PAROC framework and software platform that allows seamless design and in-silico validation of advanced controllers for complex systems are followed. The controller presented in this work is successfully tested against disturbances and is shown to efficiently capture the process periodic nature. V C 2016 American Institute of Chemical Engineers AIChE J, 62: 2341AIChE J, 62: -2357AIChE J, 62: , 2016 Keywords: process control, continuous biomanufacturing, multiparametric control IntroductionMonoclonal antibodies (mAbs) play a vital role in the treatment of infectious diseases, cancer, and autoimmune diseases.1 They are characterized, however, by high prices (approximately $35000 p/a per patient for mAbs treating cancer conditions) 2 that arise from their high production costs. Although over the past few years their market has been rapidly increasing, 3 the emergence of biosimilars and the introduction of novel therapeutic agents drives their biomanufacturing toward alternative routes of lower operating costs. 4 MAb production consists of the upstream processing (USP), where the cells are cultured and the therapeutic agent is produced, and the downstream processing (DSP) that involves the isolation/ purification steps of the targeted product. Under high titers, however, DSP can become significantly expensive, mostly due to equipment and consumables costs. [5][6][7][8][9] This along with the increasing demand on product quality and higher titers 3,5 drive advances in mAb biomanufacturing toward continuous operation. 4 Here, we focus on the development of advanced control strategies for the multicolumn countercurrent solvent gradient purification process (MCSGP), 10 aiming to drive the system toward continuous operation. MCSGP is a semicontinuous, chromatographic separation process of biomolecules, based on ion-exchange firstly presented by . 11 The process is described by complex partial differential and algebraic equations (PDAEs), involving highly nonlinear terms and is governed by periodic operation profiles that render control studies difficult to perform. There have been several works that have studied the optimization and control of such systems. Degerman et al. (2006) 12 use a nonlinear performed optimization studies on a nonlinear chromatography model to define the optimal operation points that will meet the purity Corresponding concerning this article should be addressed to E. Pistikopoulos at stratos@tamu.edu. constrai...

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The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

In an effort to provide affordable and reliable power and heat to the domestic sector, the use of cogeneration methods has been rising in the past decade. We address the issue of optimal operation of a domestic cogeneration plant powered by a natural gas, internal combustion engine via the use of explicit/multiparametric model predictive control. More specifically, we take advantage of the natural division of a combined heat and power (CHP) cogeneration system into two distinct but interoperable subsystems, namely, the power generation subsystem and the heat recovery subsystem, in order to derive a decentralized, two-mode model predictive control scheme that specifically targets the production of either electrical power or usable heat at a given time. We follow our recently developed PAROC framework for the design of the controllers, and we apply it in a decentralized manner. We show how the CHP system can efficiently operate in both modes of operation through closed-loop validation of the control scheme against a high-fidelity CHP process model.

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