Teollisuusvaihteen kokoonpanoprosessin laadun parantaminen

Paperikoneen telalle integroitujen mekaanisten käyttölaitteiden valmistuksen siirtoprojektin yhteydessä on raportoitu huomattavan paljon valmistuksen aikaisia poikkeamia. Poikkeamien juurisyyn hahmottamiseksi tehtiin kvantitatiivinen analyysi raportoiduista poikkeamista ja kvalitatiivinen analyysi kokoonpanon nykytilasta. Kokoonpanotyöstä suuri osa on tällä hetkellä arvoa jalostamatonta työtä. Tutkimuksessa havaittiin valmistusprosessissa kaikkia kokoonpanon poikkeamien perustyyppejä ja näitä poikkeamia olivat aiheuttaneet kaikki mahdolliset aiheuttajat. Etenkin hankittavien osien laatua tulisi käsitellä poikkeamien aiheuttamien kokonaiskustannusten kautta. Kokoonpanon tarvitsema tieto on olemassa täsmällisessä muodossa, mutta sen luoksepäästävyys on huono. Prosessin kuvauksella, hiljaisen tiedon kirjaamisella ja täsmällisen tiedon julkistamisella voidaan parantaa tuotannon laatua tuotetuntemuksen parantuessa. Käyttökohteen tunteminen parantaa vaatimusten ymmärtämistä. Tutkimuksessa havaittujen poikkeamien analysoinnin perusteella erityistä huomiota tulisi kiinnittää tarkastusvaiheiden olemassa oloon, sijoitukseen ja niiden laatuun. Suuri osa poikkeamista oli edennyt kokoonpanoon jonkin tarkastusvaiheen läpi. Tarkastusvaiheiden laatutason tulee parantua, jotta moninkertaisesta tarkastuksesta voidaan luopua.

Novel technology for preparation of optically active chemicals

Asymmetric synthesis using modified heterogeneous catalysts has gained lots of interest in the production of optically pure chemicals, such as pharmaceuticals, nutraceuticals, fragrances and agrochemicals. Heterogeneous modified catalysts capable of inducing high enantioselectivities are preferred in industrial scale due to their superior separation and handling properties. The topic has been intensively investigated both in industry and academia. The enantioselective hydrogenation of ethyl benzoylformate (EBF) to (R)-ethyl mandelate over (-)-cinchonidine (CD)-modified Pt/Al2O3 catalyst in a laboratory-scale semi-batch reactor was studied as a function of modifier concentration, reaction temperature, stirring rate and catalyst particle size. The main product was always (R)-ethyl mandelate while small amounts of (S)-ethyl mandelate were obtained as by product. The kinetic results showed higher enantioselectivity and lower initial rates approaching asymptotically to a constant value as the amount of modifier was increased. Additionally, catalyst deactivation due to presence of impurities in the feed was prominent in some cases; therefore activated carbon was used as a cleaning agent of the raw material to remove impurities prior to catalyst addition. Detailed characterizations methods (SEM, EDX, TPR, BET, chemisorption, particle size distribution) of the catalysts were carried out. Solvent effects were also studied in the semi-batch reactor. Solvents with dielectric constant (e) between 2 and 25 were applied. The enantiomeric excess (ee) increased with an increase of the dielectric coefficient up to a maximum followed by a nonlinear decrease. A kinetic model was proposed for the enantioselectivity dependence on the dielectric constant based on the Kirkwood treatment. The non-linear dependence of ee on (e) successfully described the variation of ee in different solvents. Systematic kinetic experiments were carried out in the semi-batch reactor. Toluene was used as a solvent. Based on these results, a kinetic model based on the assumption of different number of sites was developed. Density functional theory calculations were applied to study the energetics of the EBF adsorption on pure Pt(1 1 1). The hydrogenation rate constants were determined along with the adsorption parameters by non-linear regression analysis. A comparison between the model and the experimental data revealed a very good correspondence. Transient experiments in a fixed-bed reactor were also carried out in this work. The results demonstrated that continuous enantioselective hydrogenation of EBF in hexane/2-propanol 90/10 (v/v) is possible and that continuous feeding of (-)-cinchonidine is needed to maintain a high steady-state enantioselectivity. The catalyst showed a good stability and high enantioselectivity was achieved in the fixed-bed reactor. Chromatographic separation of (R)- and (S)-ethyl mandelate originating from the continuous reactor was investigated. A commercial column filled with a chiral resin was chosen as a perspective preparative-scale adsorbent. Since the adsorption equilibrium isotherms were linear within the entire investigated range of concentrations, they were determined by pulse experiments for the isomers present in a post-reaction mixture. Breakthrough curves were measured and described successfully by the dispersive plug flow model with a linear driving force approximation. The focus of this research project was the development of a new integrated production concept of optically active chemicals by combining heterogeneous catalysis and chromatographic separation technology. The proposed work is fundamental research in advanced process technology aiming to improve efficiency and enable clean and environmentally benign production of enantiomeric pure chemicals.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Sulfuric acid - glucose separation using mixed-recycle steady state recycling chromatography

Batch chromatography is a widely used separation technique in a variety of fields meeting difficult separations. Several technologies for improving the performance of chromatography have been studied, including mixed-recycle steady state recycling (MR-SSR) chromatography. Design of MR-SSR has been commonly limited on 100 % purity constraint cases and empirical work. In this study a predictive design method was used to optimize feed pulse size and design a number of experimental MR-SSR separations for a solution of 20 % sulfuric acid and 100 g/L glucose. The design was under target product fraction purities of 98.7 % for H2SO4 and 95 % for glucose. The experiments indicate a maximum of 59 % increase in sulfuric acid productivity and 82 % increase for glucose when compared to corresponding batch separation. Eluent consumption was lowered by approximately 50 % using recycling chromatography. Within this study the target purities and yields set in design were not completely met, and further optimization of the process is deemed necessary.