Framework for Embedding Process Simulator in GAMS via Kriging Surrogate Model Applied to C3MR Natural Gas Liquefaction Optimization

Rigorous black-box simulations are useful to describe complex systems. However, it cannot be directly integrated into mathematical programming models in some algebraic modeling environments because of the lack of symbolic formulation. In the present paper, a framework is proposed to embed the Aspen HYSYS process simulator in GAMS using kriging surrogate models to replace the simulator-dependent, black-box objective, and constraints functions. The approach is applied to the energy-efficient C3MR natural gas liquefaction process simulation optimization using multi-start nonlinear programming and the local solver CONOPT in GAMS. Results were compared with two other meta-heuristic approaches, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), and with the literature. In a small simulation evaluation budget of 20 times the number of decision variables, the proposed optimization approach resulted in 0.2538 kW of compression work per kg of natural gas and surpassed those of the PSO and GA and the previous literature from 2.45 to 15.3 %.The authors acknowledge the National Council for Scientific and Technological Development – CNPq (Brazil), processes 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, and Coordination for the Improvement of Higher Education Personnel – CAPES (Brazil) for the financial support

Multi-objective simulation optimization via kriging surrogate models applied to natural gas liquefaction process design

A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both power consumption and heat exchanger area utilization. The Pareto solutions of the single-mixed refrigerant (SMR) and propane-precooled mixed refrigerant (C3MR) processes are compared to determine the suitability of each process in terms of energy consumption and heat exchanger area. Kriging models and the ɛ-constraint methodology are used to sequentially provide simple surrogate optimization subproblems, whose minimizers are promising feasible and non-dominated solutions to the original black-box problem. The surrogate-based ɛ-constrained optimization subproblems are solved in GAMS using CONOPT. The Pareto Fronts achieved with the surrogate-based framework dominate the results from the NSGA-II, a well-established meta-heuristics of multi-objective optimization. The objective functions of non-dominated solutions go as low as 1045 and 980.3 kJ/kg-LNG and specific UA values of 212.2 and 266.9 kJ/(°C kg-LNG) for SMR and C3MR, respectively. The trade-off solutions that present the minimum sum of relative objectives are analyzed as well as the dominance of C3MR over SMR at low power consumption values and conversely at low heat exchanger area utilization.The authors LFS, CBBC, and MASSR acknowledge the National Council for Scientific and Technological Development–CNPq (Brazil), processes 200305/2020-4, 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, 307958/2021-3 and Coordination for the Improvement of Higher Education Personnel–CAPES (Brazil) for the financial support. The author JAC acknowledges financial support from the “Generalitat Valenciana” under project PROMETEO 2020/064 and the Ministerio de Ciencia e Innovación , under project PID2021-124139NB-C21

MINLP model for work and heat exchange networks synthesis considering unclassified streams

The optimal synthesis of work and heat exchange networks (WHENs) is deeply important to achieve simultaneously high energy efficiency and low costs in chemical processes via work and heat integration of process streams. This paper presents an efficient MINLP model for optimal WHENs synthesis derived from a superstructure that considers unclassified streams. The derived model is solved using BARON global optimization solver. The superstructure considers multi-staged heat integration with isothermal mixing, temperature adjustment with hot or cold utility, and work exchange network for streams that are not classified a priori. The leading advantage of the present optimization model is the capability of defining the temperature and pressure route, i.e. heating up, cooling down, expanding, or compressing, of a process stream entirely during optimization while still being eligible for global optimization. The present approach is tested to a small-scale WHEN problem and the result surpassed the ones from the literature.The authors LFS, CBBC, and MASSR acknowledge the National Council for Scientific and Technological Development – CNPq (Brazil), processes 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, and Coordination for the Improvement of Higher Education Personnel – CAPES (Brazil) for the financial support. The author JAC acknowledge financial support from the “Generalitat Valenciana” under project PROMETEO 2020/064

The role of natural regeneration to ecosystem services provision and habitat availability: a case study in the Brazilian Atlantic Forest

Natural regeneration provides multiple benefits to nature and human societies, and can play a major role in global and national restoration targets. However, these benefits are context specific and impacted by both biophysical and socioeconomic heterogeneity across landscapes. Here we investigate the benefits of natural regeneration for climate change mitigation, sediment retention and biodiversity conservation in a spatially explicit way at very high resolution for a region within the global biodiversity hotspot of the Atlantic Forest. We classified current land-use cover in the region and simulated a natural regeneration scenario in abandoned pasturelands, areas where potential conflicts with agricultural production would be minimized and where some early stage regeneration is already occurring. We then modelled changes in biophysical functions for climate change mitigation and sediment retention, and performed an economic valuation of both ecosystem services. We also modelled how land-use changes affect habitat availability for species. We found that natural regeneration can provide significant ecological and social benefits. Economic values of climate change mitigation and sediment retention alone could completely compensate for the opportunity costs of agricultural production over 20 years. Habitat availability is improved for three species with different dispersal abilities, although by different magnitudes. Improving the understanding of how costs and benefits of natural regeneration are distributed can be useful to design incentive structures that bring farmers’ decision making more in line with societal benefits. This alignment is crucial for natural regeneration to fulfil its potential as a large-scale solution for pressing local and global environmental challenges

Dance training improves cytokine secretion and viability of neutrophils in diabetic patients

Background. Evidence suggests that exercise improves neutrophil function. The decreased functional longevity of neutrophils and their increased clearance from infectious sites contribute to the increased susceptibility to infection and severity of infection observed in patients with diabetes. Objective. Herein, we investigated the effects of a dance program on neutrophil number, function, and death in type 2 diabetes mellitus (T2DM) patients and healthy volunteers. Methods. Ten patients with T2DM and twelve healthy individuals participated in a moderate-intensity dance training program for 4 months. The plasma levels of leptin, free fatty acids (FFAs), tumour necrosis factor-α (TNF-α), C-reactive protein (CRP), interleukin-1β (IL-1β), and interleukin-1 receptor antagonist (IL-1ra); neutrophil counts; extent of DNA fragmentation; cell membrane integrity; and production of TNF-α, interleukin-8 (IL-8), interleukin-6 (IL-6), and IL-1β in neutrophils were measured before and after training. Results. Training reduced plasma levels of TNF-α (1.9-fold in controls and 2.2-fold in patients with T2DM) and CRP (1.4-fold in controls and 3.4-fold in patients with T2DM). IL-1ra levels were higher in the control group (2.2-fold) after training. After training, neutrophil DNA fragmentation was decreased in patients with T2DM (90%), while the number of neutrophils increased (70% in controls and 1.1-fold in patients with T2DM). Conclusion. Dance training is a nonpharmacological strategy to reduce inflammation and improve neutrophil clearance in patients with T2DM

Master Sculptor at Work: Enteropathogenic Escherichia coli Infection Uniquely Modifies Mitochondrial Proteolysis during Its Control of Human Cell Death

Enteropathogenic Escherichia coli (EPEC) causes severe diarrheal disease and is present globally. EPEC virulence requires a bacterial type III secretion system to inject 20 effector proteins into human intestinal cells. Three effectors travel to mitochondria and modulate apoptosis; however, the mechanisms by which effectors control apoptosis from within mitochondria are unknown. To identify and quantify global changes in mitochondrial proteolysis during infection, we applied the mitochondrial terminal proteomics technique mitochondrial stable isotope labeling by amino acids in cell culture-terminal amine isotopic labeling of substrates (MS-TAILS). MS-TAILS identified 1,695 amino N-terminal peptides from 1,060 unique proteins and 390 N-terminal peptides from 215 mitochondrial proteins at a false discovery rate of 0.01. Infection modified 230 cellular and 40 mitochondrial proteins, generating 27 cleaved mitochondrial neo-N termini, demonstrating altered proteolytic processing within mitochondria. To distinguish proteolytic events specific to EPEC from those of canonical apoptosis, we compared mitochondrial changes during infection with those reported from chemically induced apoptosis. During infection, fewer than half of all mitochondrial cleavages were previously described for canonical apoptosis, and we identified nine mitochondrial proteolytic sites not previously reported, including several in proteins with an annotated role in apoptosis, although none occurred at canonical Asp-Glu-Val-Asp (DEVD) sites associated with caspase cleavage. The identification and quantification of novel neo-N termini evidences the involvement of noncaspase human or EPEC protease(s) resulting from mitochondrialtargeting effectors that modulate cell death upon infection. All proteomics data are available via ProteomeXchange with identifier PXD016994. IMPORTANCE To our knowledge, this is the first study of the mitochondrial proteome or N-terminome during bacterial infection. Identified cleavage sites that had not been previously reported in the mitochondrial N-terminome and that were not generated in canonical apoptosis revealed a pathogen-specific strategy to control human cell apoptosis. These data inform new mechanisms of virulence factors targeting mitochondria and apoptosis during infection and highlight how enteropathogenic Escherichia coli (EPEC) manipulates human cell death pathways during infection, including candidate substrates of an EPEC protease within mitochondria. This understanding informs the development of new antivirulence strategies against the many human pathogens that targe

Self-Organization and Complex Networks

In this chapter we discuss how the results developed within the theory of fractals and Self-Organized Criticality (SOC) can be fruitfully exploited as ingredients of adaptive network models. In order to maintain the presentation self-contained, we first review the basic ideas behind fractal theory and SOC. We then briefly review some results in the field of complex networks, and some of the models that have been proposed. Finally, we present a self-organized model recently proposed by Garlaschelli et al. [Nat. Phys. 3, 813 (2007)] that couples the fitness network model defined by Caldarelli et al. [Phys. Rev. Lett. 89, 258702 (2002)] with the evolution model proposed by Bak and Sneppen [Phys. Rev. Lett. 71, 4083 (1993)] as a prototype of SOC. Remarkably, we show that the results obtained for the two models separately change dramatically when they are coupled together. This indicates that self-organized networks may represent an entirely novel class of complex systems, whose properties cannot be straightforwardly understood in terms of what we have learnt so far.Comment: Book chapter in "Adaptive Networks: Theory, Models and Applications", Editors: Thilo Gross and Hiroki Sayama (Springer/NECSI Studies on Complexity Series