Sustainable ammonia production through process synthesis and global optimization

Current ammonia production technologies have a significant carbon footprint. In this study, we present a process synthesis and global optimization framework to discover the efficient utilization of renewable resources in ammonia production. Competing technologies are incorporated in a process superstructure where biomass, wind, and solar routes are compared with the natural gas-based reference case. A deterministic global optimization-based branch-and-bound algorithm is used to solve the resulting large-scale nonconvex mixed-integer nonlinear programming problem (MINLP). Case studies for Texas, California, and Iowa are conducted to examine the effects of different feedstock prices and availabilities. Results indicate that profitability of ammonia production is highly sensitive to feedstock and electricity prices, as well as greenhouse gas (GHG) restrictions. Under strict 75% GHG reductions, biomass to ammonia route is found to be competitive with natural gas route, whereas wind and solar to ammonia routes require further improvement to compete with those two routes. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16498 2019     

Operability and control in process intensification and modular design: Challenges and opportunities

In this article, the importance of considering operability and control criteria in the analysis and design of intensified and modular processes is discussed. We first analyze the impact on operability of key factors including: (i) degrees of freedom, (ii) process constraints, (iii) numbering up vs. scaling up, and (iv) dynamic/periodic operation. Comparative examples are presented to showcase the pros and cons in intensified/modular systems vs. their conventional counterparts from operability and control aspects. Then we look into metrics and tools to address these challenges such as: (i) flexibility analysis, (ii) operability-based design, and (iii) advanced model-based control. Considering different conceptual design stages as synthesis intensification, steady-state design, and dynamic operational optimization, we highlight the need to incorporate different levels of operability considerations. Future research opportunities and perspectives are also identified, particularly emphasizing the importance of a holistic strategy for integrated design, operability, and control of intensified and modular process systems.     

Neural networks and response surface polynomials for design of vehicle joints

Typically, design of a complex system starts by setting targets for its performance characteristics. Then, design engineers cascade these targets to the components and design the components to meet these targets. It is important to have efficient tools that check if a set of performance targets for a component corresponds to a feasible design and determine the dimensions and mass of this design. This paper describes a method to develop tools that relate response parameters that describe the performance of a component to the physical design variables that specify its geometry. Neural networks and response surface polynomials are used to rapidly predict the performance characteristics of the components given the component dimensions. The method is demonstrated on design of an automotive joint. The paper compares neural networks and response surface polynomials and shows that they are almost equally accurate for the problem considered.     

Dielectric spectroscopy and gas chromatography methods applied on high-voltage transformer oils

This paper points out the advantages offered by the temperature dependent dielectric spectroscopy in frequency domain, as an additional diagnostic technique for insulation testing of power transformers for life-time prediction. Complex permittivity measurements have been carried out as a function of operating frequency and oil temperature for a wide selection of high voltage transformers and the gaseous byproduct concentrations were also monitored. The work correlates dielectrometry data to the gas chromatography ones.     

Design of robust model-based controllers via parametric programming

In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller is derived off-line via parametric programming before any actual process implementation takes place. The proposed control scheme guarantees feasible operation in the presence of bounded input uncertainties by (i) explicitly incorporating in the controller design stage a set of feasibility constraints and (ii) minimizing the nominal performance, or the expectation of the performance over the uncertainty space. An extension of the method to problems involving target point tracking in the presence of persistent disturbances is also discussed. The general concept is illustrated with two examples.     

Probing Protein Folding with Sequence-Reversed α-Helical Bundles

Recurrent protein folding motifs include various types of helical bundles formed by α-helices that supercoil around each other. While specific patterns of amino acid residues (heptad repeats) characterize the highly versatile folding motif of four-α-helical bundles, the significance of the polypeptide chain directionality is not sufficiently understood, although it determines sequence patterns, helical dipoles, and other parameters for the folding and oligomerization processes of bundles. To investigate directionality aspects in sequence-structure relationships, we reversed the amino acid sequences of two well-characterized, highly regular four-α-helical bundle proteins and studied the folding, oligomerization, and structural properties of the retro-proteins, using Circular Dichroism Spectroscopy (CD), Size Exclusion Chromatography combined with Multi-Angle Laser Light Scattering (SEC-MALS), and Small Angle X-ray Scattering (SAXS). The comparison of the parent proteins with their retro-counterparts reveals that while the α-helical character of the parents is affected to varying degrees by sequence reversal, the folding states, oligomerization propensities, structural stabilities, and shapes of the new molecules strongly depend on the characteristics of the heptad repeat patterns. The highest similarities between parent and retro-proteins are associated with the presence of uninterrupted heptad patterns in helical bundles sequences.     

Integrated process design,scheduling, and model predictive control of batch processes with closed-loop implementation

Simultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed-integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed-loop implementation. We use multi-parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base-2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.     

Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.