Periodic Operation with Modulation of Inlet Concentration and Flow Rate Part I: Nonisothermal Continuous Stirred-Tank Reactor

The nonlinear frequency response (NFR) method, which is an analytical, fast, and easy method for evaluating the performance of forced periodically operated chemical reactors, was used to investigate possible improvements to a nonisothermal continuous stirred tank reactor (CSTR) when inlet concentration and/or flow rate is periodically modulated. The product yield corresponding to periodic operation is defined, expressions for its estimation, based on the NFR method, are derived, and it is used to evaluate the performance improvements due to periodic operation. Part I considers the general nonisothermal case. In Part II, these results are applied to an adiabatic CSTR and used to evaluate possible improvements for the case of the hydrolysis reaction of acetic anhydride.This is the peer-reviewed version of the article: Daliborka Nikolić, Andreas Seidel‐Morgenstern, Menka Petkovska, Periodic Operation with Modulation of Inlet Concentration and Flow Rate. Part I: Nonisothermal Continuous Stirred‐Tank Reactor, Chemical Engineering & Technology, 2016, 39, 11, 2020-2028, [ https://doi.org/10.1002/ceat.201600185]The published version: [https://cer.ihtm.bg.ac.rs/handle/123456789/1889

A LTL Fragment for GR(1)-Synthesis

The idea of automatic synthesis of reactive programs starting from temporal logic (LTL) specifications is quite old, but was commonly thought to be infeasible due to the known double exponential complexity of the problem. However, new ideas have recently renewed the interest in LTL synthesis: One major new contribution in this area is the recent work of Piterman et al. who showed how polynomial time synthesis can be achieved for a large class of LTL specifications that is expressive enough to cover many practical examples. These LTL specifications are equivalent to omega-automata having a so-called GR(1) acceptance condition. This approach has been used to automatically synthesize implementations of real-world applications. To this end, manually written deterministic omega-automata having GR(1) conditions were used instead of the original LTL specifications. However, manually generating deterministic monitors is, of course, a hard and error-prone task. In this paper, we therefore present algorithms to automatically translate specifications of a remarkable large fragment of LTL to deterministic monitors having a GR(1) acceptance condition so that the synthesis algorithms can start with more readable LTL specifications

Isolation of soybean protein P34 from oil bodies using hydrophobic interaction chromatography

<p>Abstract</p> <p>Background</p> <p>Soybeans play a prominent role in allergologic research due to the high incidence of allergic reactions. For detailed studies on specific proteins it is necessary to have access to a large amount of pure substance.</p> <p>Results</p> <p>In this contribution, a method for purifying soybean (<it>Glycine max</it>) protein P34 (also called Gly m Bd 30 K or Gly m 1) using hydrophobic interaction chromatography is presented. After screening experiments using 1 mL HiTrap columns, Butyl Sepharose 4 FF was selected for further systematic investigations. With this stationary phase, suitable operation conditions for two-step gradient elution using ammonium sulphate were determined experimentally. The separation conditions obtained in a small column could be scaled up successfully to column volumes of 7.5 and 75 mL, allowing for high product purities of almost 100% with a yield of 27% for the chromatographic separation step. Conditions could be simplified further using a onestep gradient, which gave comparable purification in a shorter process time. The identity of the purified protein was verified using in-gel digestion and mass spectrometry as well as immunological techniques.</p> <p>Conclusion</p> <p>With the technique presented it is possible to produce, within a short timeframe, pure P34, suitable for further studies where an example antigen is needed.</p

Protonation of Pyruvic Acid - Synthesis of a plain Superelectrophile

The syntheses of [H3C(O)CC(OH)(2)][MF6] and [H3C(OH)CC-(OH)(2)][MF6](2) (M=As, Sb) by reacting pyruvic acid in the superacidic systems HF/AsF, and HF/SbF5 are reported. The salts were characterized by low-temperature vibrational spectroscopy and in the cases of [H3C(O)CC(OH)(2)][SbF6] and [H3C(OH)CC-(OH)(2)][SbF6](2)center dot HF by X-ray crystal structure analyses. The exper- imental results are discussed together with quantum chemical calculations. Remarkably, the bond distance and the twisting angle around the central C-C bond are unaffected by the protonations despite increasing coulombic repulsion. The crystal structure reveals short interionic interactions that have a considerable influence on the C-C bond

Parameter Identifiability of Artemisinin Synthesis using Design of Experiments

Artemisinin-based combination therapies are recommended by the World Health Organization to treat malaria, one of the most abundant infectious diseases in the world. Recently, a novel production route, which combines the extraction and the catalyzed chemical synthesis, has been shown to be a promising sustainable processing alternative [Triemer, 2018]. To exploit its mechanism, operational settings and limits, mathematical modeling might be beneficial when thorough system insight is required. In a first step, we consider the catalyzed synthesis step from dihydroartemisinic acid to artemisinin, and we show that only a subset of the parameters of the considered model is identifiable with the available sparse data using a singular value decomposition approach. In a second step, within the framework of design of experiments (DoE), we demonstrate the effect of additional experimental data to overcome the non-identifiability problem of the model parameters

Detailed Kinetic Model for the Reaction of Ethene to Propene on Ni/AlMCM-41

The Ni/AlMCM-41 was prepared and applied as the catalyst for the direct conversion of ethene to propene. Based on the results of the broad experimental study, two reaction networks were compared, one consisting of dimerization, isomerization and metathesis and a modified network suggesting the cracking of long-chain olefins. To correlate the experimentally obtained data, the classical Langmuir-Hinshelwood-Hougen-Watson model was applied for both reaction networks. The second network involving catalytic cracking offers a satisfying prediction of the observed product distributions

Exploiting the Temporal Logic Hierarchy and the Non-Confluence Property for Efficient LTL Synthesis

The classic approaches to synthesize a reactive system from a linear temporal logic (LTL) specification first translate the given LTL formula to an equivalent omega-automaton and then compute a winning strategy for the corresponding omega-regular game. To this end, the obtained omega-automata have to be (pseudo)-determinized where typically a variant of Safra's determinization procedure is used. In this paper, we show that this determinization step can be significantly improved for tool implementations by replacing Safra's determinization by simpler determinization procedures. In particular, we exploit (1) the temporal logic hierarchy that corresponds to the well-known automata hierarchy consisting of safety, liveness, Buechi, and co-Buechi automata as well as their boolean closures, (2) the non-confluence property of omega-automata that result from certain translations of LTL formulas, and (3) symbolic implementations of determinization procedures for the Rabin-Scott and the Miyano-Hayashi breakpoint construction. In particular, we present convincing experimental results that demonstrate the practical applicability of our new synthesis procedure

The Limitations of Optimization from Samples

In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them? We formalize this question through a novel framework we call optimization from samples (OPS). In OPS, we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint. While there are interesting classes of functions that can be optimized from samples, our main result is an impossibility. We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable. In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution. We also show tight approximation guarantees for maximization under a cardinality constraint of several interesting classes of functions including unit-demand, additive, and general monotone submodular functions, as well as a constant factor approximation for monotone submodular functions with bounded curvature