Purpose – the purpose of this study is to investigate managerial and organizational barriers that are associated with artificial intelligence (AI) and develop a structured process to overcome the organizational barriers throughout different phases of the implementation process. Method – The study applied a qualitative research approach that consisted of multiple case studies from various organizations in traditional industries. Each organization worked with an AI-projects that were based on application of machine learning (ML).  The respondents came from various positions from the AI-project and were interviewed. The collected data was analyzed by using a thematic analysis with 17 interviews in total.  Findings – The study found four barriers in total from pre-implementation, implementation, and post-implementation phases of AI. These were: lack of use-case definition, low ai-knowledge, missing appropriate data, and end-user misalignment. The study would present key-activities to overcome the AI-barriers categorized that are presented in three phases: defining AI-transformation, anchoring AI-implementation and optimizing AI-usage. Theoretical contribution - Firstly, the study highlighted underlying implementation barriers in traditional industries that were business and managerial related. Secondly, the study contributed with an empirically rooted structured AI-implementation process framework. These findings extend current dialogues in the literature on challenges related to AI and connect them to specific phases in the AI-implementation process. These findings also extend current dialogues in the literature on challenges related to AI and connect them to specific phases in the AI-implementation process. Practical Implications - The practical implication of this study highlighted that there existed a lack of clearly defined strategies for implementing AI-solutions in traditional industries which this study covers by developing a basis to build on. Limitations and Future research - The study investigated a handful of organizations in different industries. Because of time- and resource constraints, increased research scope could provide more insightful perspectives which could be beneficial. In addition, because the study itself was based as a qualitative study, the methodology of the project could be prone to inconsistencies or a lack of coherence. As this approach was based on phrasing, some phrases may not be able to capture the full meaning of what was articulated. For future research proposals, a quantitative research method of this subject could give further breadth to the literature by investigating likely correlations.

<p>In the semi-enclosed Baltic Sea, excessive filamentous macro-algal biomass growth as a result of eutrophication is an increasing environmental problem. Drifting huge masses of red algae of the genera <em>Polysiphonia</em>, <em>Rhodomela</em>, and <em>Ceramium</em> accumulate on the open shore, up to five tones of algae per meter beach. During the aerobic decomposition of these algal bodies, large quantities of red colored effluents leak into the water what are toxic for the marine environment. In this study, feasibility of anaerobic conversion of red algae <em>Polysiphonia</em>, rich in nitrogen and phosphorous, was investigated. Biogas and methane potential of <em>Polysiphonia</em>, harvested in two different seasons [October and March], was investigated through three different batch digestion experiments and laboratory scale CSTR [continuous stirred tank reactor] at mesophilic (37^oC) condition. Autoclavation [steam and heat] and ultrasound pretreatments were applied in order to enhance the biodegradation. In STR, anaerobic codigestion of algal biomass with SS [sewage sludge] was applied with a gradual increase in organic loading rate [1.5-4.0 g VS/L/day] and operated for 117 days at 20days HRT [hydraulic retention time]. Reactor digestate was analyzed four times over the period to determine the nutrients and heavy metals content. It is concluded that the methane potential of algae harvested in October is almost two-fold than that of algae harvested in March, probably due to it’s higher [more than double] nitrogen richness. An increase in biogas yield was observed upto 28% and VS reduction was increased from 37% to 45% due to autoclave pretreatment. Ultrasound pretreatment had no effect on digestion. In batch digestion, maximum methane yield 0.25 m³/kg VS added at 273^oK, was obtained from algae [harvested in October] pretreated in autoclave. Codigestion of algae with SS worked well in STR with a comparatively lower OLR. At a higher OLR, methanogens were inhibited due to increased VFAs accumulation and decreased pH. A maximum biogas yield 0.49 m³/kg VS added at 310^oK , was obtained from algae [harvested in October] pretreated with autoclave. The methane content of the produced biogas was 54%. Average [over a short period, day 99-107, reactor showed steady performance] maximum biogas yields from untreated algae obtained 0.44 m3/kg VSadded at 310^oK and the VS reduction was calculated 32%. Digestate, to be used as a fertilizer, was found NH₄-N, N, P, K, S and Na rich and only Cadmium level was above the maximal limit among the heavy metals. The sand content in algae during harvesting was considered as a factor to disrupt the operation. Codigestion of <em>Polysiphonia</em> algal biomass with substrate with higher C:N ratio like paper mill waste should be more appropriate to increase the methane and biogas yield. It is inconclusive whether AD process is a good method to dewater redalgae or not but large scale harvesting of algae will definitely contribute to curb eutrophication of the Baltic Sea through decreasing N and P level.</p>

In the semi-enclosed Baltic Sea, excessive filamentous macro-algal biomass growth as a result of eutrophication is an increasing environmental problem. Drifting huge masses of red algae of the genera Polysiphonia, Rhodomela, and Ceramium accumulate on the open shore, up to five tones of algae per meter beach. During the aerobic decomposition of these algal bodies, large quantities of red colored effluents leak into the water what are toxic for the marine environment. In this study, feasibility of anaerobic conversion of red algae Polysiphonia, rich in nitrogen and phosphorous, was investigated. Biogas and methane potential of Polysiphonia, harvested in two different seasons [October and March], was investigated through three different batch digestion experiments and laboratory scale CSTR [continuous stirred tank reactor] at mesophilic (37oC) condition. Autoclavation [steam and heat] and ultrasound pretreatments were applied in order to enhance the biodegradation. In STR, anaerobic codigestion of algal biomass with SS [sewage sludge] was applied with a gradual increase in organic loading rate [1.5-4.0 g VS/L/day] and operated for 117 days at 20days HRT [hydraulic retention time]. Reactor digestate was analyzed four times over the period to determine the nutrients and heavy metals content. It is concluded that the methane potential of algae harvested in October is almost two-fold than that of algae harvested in March, probably due to it’s higher [more than double] nitrogen richness. An increase in biogas yield was observed upto 28% and VS reduction was increased from 37% to 45% due to autoclave pretreatment. Ultrasound pretreatment had no effect on digestion. In batch digestion, maximum methane yield 0.25 m3/kg VS added at 273oK, was obtained from algae [harvested in October] pretreated in autoclave. Codigestion of algae with SS worked well in STR with a comparatively lower OLR. At a higher OLR, methanogens were inhibited due to increased VFAs accumulation and decreased pH. A maximum biogas yield 0.49 m3/kg VS added at 310oK , was obtained from algae [harvested in October] pretreated with autoclave. The methane content of the produced biogas was 54%. Average [over a short period, day 99-107, reactor showed steady performance] maximum biogas yields from untreated algae obtained 0.44 m3/kg VSadded at 310oK and the VS reduction was calculated 32%. Digestate, to be used as a fertilizer, was found NH4-N, N, P, K, S and Na rich and only Cadmium level was above the maximal limit among the heavy metals. The sand content in algae during harvesting was considered as a factor to disrupt the operation. Codigestion of Polysiphonia algal biomass with substrate with higher C:N ratio like paper mill waste should be more appropriate to increase the methane and biogas yield. It is inconclusive whether AD process is a good method to dewater redalgae or not but large scale harvesting of algae will definitely contribute to curb eutrophication of the Baltic Sea through decreasing N and P level.

This diploma thesis focuses on improvement of the existing technology of the flange part in cooperation with the company Znojemské strojírny, s r.o. The theoretical part describes the technologies used in machining of the component. Subsequently, cutting materials and aluminum alloys are dealt with. A detailed analysis of the existing technology is performed in the practical part of the thesis followed by rationalization of the production process, which was carried out with respect to time and financial savings. A new technological procedure is developed after the improvement of the current production process. Finally, a technical-economic evaluation of the design improvement process is carried out.

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.