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Vorgestellt wird ein Konzept zur biologischen Methanisierung von Wasserstoff direkt in Biogasreaktoren, mit dem durch Membranbegasung der Methangehalt des Biogases auf > 96 % erhöht werden kann. Essentiell zum Erreichen solch hoher Methanwerte sind die Einhaltung eines optimalen pH-Bereichs und die Vermeidung von H2-Akkumulation. Im Falle einer Limitierung der Methanbildungsrate durch den eigentlichen anaeroben Abbauprozess der Biomasse ist auch eine externe Zufuhr von CO2 zur weiteren Methanbildung denkbar. Das Verfahren soll weiter optimiert und in einem von der Deutschen Bundesstiftung Umwelt geförderten Projekt in der Biogasanlage einer regionalen Käserei in der Praxis getestet werden. Die hier angestrebte Kombination aus dezentraler Abfallverwertung und Eigenenergieerzeugung eines lebensmittelverarbeitenden Betriebs unter Einbindung in ein intelligentes Erneuerbare Energien - Konzept soll einen zusätzlichen Mehrwert liefern.
Durch eine stetige Preissteigerung der fossilen Energieträger werden auch im Bereich der mobilen Arbeitsmaschinen neben einer hohen Zuverlässig u.a. Forderungen nach steigenden Gesamtwirkungsgraden, mit der hierdurch einhergehenden Energieeffizienz, forciert. Auch bei mobilen Arbeitsmaschinen ist der häufig eingeschränkt zur Verfügung stehende Bauraum für Traktionsantriebe eine Herausforderung. Ziel dieser Veröffentlichung ist ein allgemeingültiger Vergleich verschiedener elektrischer Antriebsarten als Traktionsantrieb für mobile Arbeitsmaschinen.
A system for the on-line/in-line measurement of soot particle sizes and concentrations in the undiluted exhaust gas of diesel engines was developed and successfully tested. The unit uses the individual attenuations of three different laser wavelengths and is combined with an optical cell (white principle) with adjustable path lengths from 2.5 to 15 meters.
Time Resolved Measurements of Soot Concentrations and Mean Particle Sizes during EUDC and ECE Cycles
(2002)
The purpose of this study was to describe the effects of running speed and slope on metatarsophalangeal (MTP) joint kinematics. 22 male and female runners underwent 3D motion analysis on an instrumented treadmill at three different speeds (2.5 m/s, 3.0 m/s, 3.5 m/s). At each speed, participants ran at seven slope conditions (downhill: -15%, -10%, -5%, level, and uphill: +5%, +10%, +15%). We found a significant main effect (p < 0.001) of running speed and slope on peak MTP dorsiflexion and a running speed by slope interaction effect (p < 0.001) for peak MTP dorsiflexion velocity. These findings highlight the need to consider running intensity and environmental factors like running surface inclination when considering MTP joint mechanics and technological aids to support runners.
The identification and quantification of compounds in the gas phase becomes of increasing interest in the context of environmental protection, as well as in the analytical field. In this respect, the high extinction coefficients of vapours and gases in the ultraviolet wavelength region allow a very sensitive measurement system. In addition, the increased performance of the components necessary for setting up a measurement system, such as fibres, light sources and detectors has been improved. In particular the light sources and detectors offer improved stability, and the deep UV performance and solarisation resistance of fused silica fibres allow have been significantly optimized in the past years. Therefore a compact and reliable detection system with high measuring accuracy is developed. Within this paper possible applications of the system under development and recent results will be discussed.
Die angestrebten Klimaschutzziele erfordern, dass Erneuerbare Energien längerfristig zur Hauptenergiequelle der Energieversorgung werden. Um dieses ehrgeizige Ziel zu erreichen, ist es angebracht konventionelle und erneuerbare Energie oder noch besser nachhaltige Einzelprozesse intelligent miteinander zu verknüpfen.
Das Projekt EBIPREP wird von einer interdisziplinären Forschergruppe bestehend aus Chemikern, Prozessingenieuren und Bioprozessingenieuren sowie Physikern, die auf Sensoren und Prozesssteuerung spezialisiert sind durchgeführt. Das Ziel ist es, neue Lösungen für die Nutzungswege von Holzhackschnitzeln und den bei der mechanischen Trocknung anfallenden Holzpresssaft zu entwickeln. Neben der Hackschnitzelvergasung und der katalytischen Reinigung des Holzgases steht die Nutzung des Holzpresssafts in Biogasanlagen und bei der biotechnologischen Wertstofferzeugung, z.B. bei der Enzymherstellung, im Vordergrund.
Was wir tun?
Das EBIPREP-Projekt wird von einer interdisziplinären Forschungsgruppe durchgeführt, die sich aus Chemikern, Prozessingenieuren, Bioprozessingenieuren und Physikern zusammensetzt. Ziel ist es, neue Lösungen für den Einsatz von Hackschnitzeln und Holzpresssaft zu entwickeln, die durch ein innovatives mechanisches Trocknungsverfahren gewonnen werden. Neben der Holzvergasung und katalytischen Reinigung des Holzgases ist der Einsatz von Holzpresssaft in Biogasanlagen und in biotechnologischen Produktionsprozessen von Wertstoffen vorgesehen. Holzhackschnitzel werden thermisch vergast. Es werden Online-Sensoren entwickelt, um die relevanten Parameter der stabilisierten und optimierten Einzelprozesse auszuwerten. Die Verknüpfung von thermischen und biotechnologischer Konversionsprozessen könnte dazu beitragen, die Dimension von Biogasreaktoren erheblich zu reduzieren. Diese Tatsache wird folglich zu einer spürbaren Kostensenkung führen.
Ziele des EBIPREP-Projekts
• die Vorteile der thermischen und biologischen Umwandlung von Biomasse zu kombinieren;
• Entwicklung eines Verfahrens zur Reduzierung von Schadstoffemissionen mit innovativen Sensoren und katalytische Behandlung von Synthesegasen;
• nachhaltige Produktion biotechnologischer wertvoller Produkte
• wirtschaftliche und ökologische Analyse des Gesamtprozesses im Vergleich zu den Einzelprozessen
• Einsatz von Prozessabwässern zur Erzeugung regenerativer Energie oder biotechnologischer Wertstoffe
• Erwerb neuer Kenntnisse auf dem Gebiet der Rückgewinnungstechnik von Rückständen
• und Energieerzeugung;
• Erweiterung neuer Anwendungsfelder für innovative Sensoren und Keramik
• Schäume für Katalysatoren;
• Senkung der Kosten für die Biogasproduktion
Im geplanten Übersichtsvortrag werden die vernetzten Strukturen des Projekts EBIPREP und deren zentralen Ergebnisse vorgestellt.
Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper.
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
Colored glass products with various printing technologies are becoming more important in industry. The aim is to achieve individual solution in a very short delivery time. Conventional thermal treatment of burning glasses in oven for tempered color printing has predominant issues with high time consumption, energy consumption and manufacturing cost. It requires alternative process development.
This paper proposes laser process to overcome issues in conventional treatment with the latest results of tempering colored glass. Samples have been analyzed with the scanning electron microscope (SEM). Two different laser systems have been applied and the glass has been printed with black paste.
During the coronavirus crisis, labs had to be offered in digital form in mechanical engineering at short notice. For this purpose, digital twins of more complex test benches in the field of fluid energy machines were used in the mechanical engineering course, with which the students were able to interact remotely to obtain measurement data. The concept of the respective lab was revised with regard to its implementation as a remote laboratory. Fortunately, real-world labs were able to be fully replaced by remote labs. Student perceptions of remote labs were mostly positive. This paper explains the concept and design of the digital twins and the lab as well as the layout, procedure, and finally the results of the accompanying evaluation. However, the implementation of the digital twins to date does not yet include features that address the tactile experience of working in real-world labs.
For the RoboCup Soccer AdultSize League the humanoid robot Sweaty uses a single fully convolutional neural network to detect and localize the ball, opponents and other features on the field of play. This neural network can be trained from scratch in a few hours and is able to perform in real-time within the constraints of computational resources available on the robot. The time it takes to precess an image is approximately 11 ms. Balls and goal posts are recalled in 99 % of all cases (94.5 % for all objects) accompanied by a false detection rate of 1.2 % (5.2 % for all). The object detection and localization helped Sweaty to become finalist for the RoboCup 2017 in Nagoya.
One of the challenges in humanoid robotics is motion control. Interacting with humans requires impedance control algorithms, as well as tackling the problem of the closed kinematic chains which occur when both feet touch the ground. However, pure impedance control for totally autonomous robots is difficult to realize, as this algorithm needs very precise sensors for force and speed of the actuated parts, as well as very high sampling rates for the controller input signals. Both requirements lead to a complex and heavy weight design, which makes up for heavy machines unusable in RoboCup Soccer competitions.
A lightweight motor controller was developed that can be used for admittance and impedance control as well as for model predictive control algorithms to further improve the gait of the robot.
Autonomous humanoid robots require light weight, high torque and high speed actuators to be able to walk and run. For conventional gears with a fixed gear ratio the product of torque and velocity is constant. On the other hand desired motions require maximum torque and speed. In this paper it is shown that with a variable gear ratio it is possible to vary the relation between torque and velocity. This is achieved by introducing systems of rods and levers to move the joints of our humanoid robot ”Sweaty II”. On the basis of a variable gear ratio low speed and high torque can be achieved for those joint angles, which require this motion mode, whereas high speed and low torque can be realized for those joint angles, where it is favorable for the desired motion.
Microscale trigeneration systems are highly flexible in their operation and thus offer the technical possibility for peak load shifting in building demand side management. However to harness their potential modern control methods such as model predictive control must be implemented for their optimal scheduling. In literature the need for experimental investigation of microscale trigeneration systems to identify typical characteristics of the components and their interactions has been identified. On a real-life setup control specific information of the components is collected and lessons learnt during commissioning of the equipment is shared. The data is analysed to draw the vital characteristics of the system and it will be used for creating models of the components that can be utilised for optimal control.
The transformation of the building energy sector to a highly efficient, clean, decentralised and intelligent system requires innovative technologies like microscale trigeneration and thermally activated building structures (TABS) to pave the way ahead. The combination of such technologies however presents a scientific and engineering challenge. Scientific challenge in terms of developing optimal thermo-electric load management strategies based on overall energy system analysis and an engineering challenge in terms of implementing these strategies through process planning and control. Initial literature research has pointed out the need for a multiperspective analysis in a real life laboratory environment. To this effect an investigation is proposed wherein an analytical model of a microscale trigeneration system integrated with TABS will be developed and compared with a real life test-rig corresponding to building management systems. Data from the experimental analysis will be used to develop control algorithms using model predictive control for achieving the thermal comfort of occupants in the most energy efficient and grid reactive manner. The scope of this work encompasses adsorption cooling based microscale trigeneration systems and their deployment in residential and light commercial buildings.
We herein present a topology design method based on local optimality criteria which has been implemented in an open source Navier-Stokes solver for turbulent flows. Our method aims for the fast generation of geometry proposals in the early conceptual phase. To the best of our knowledge, this is the first local criteria approach utilizing a wall function turbulence model in order to consider turbulent flows. In order to allow for the growth as well as the shrinkage, or even the formation or disappearance of structural features, a topological approach is chosen. By introducing a volume fraction parameter, we distinguish between fluid and solid properties in each control volume. The fluid-solid interface is represented by an immersed boundary method using a piecewise linear surface reconstruction.
To achieve Germany's climate targets, the industrial sector, among others, must be transformed. The decarbonization of industry through the electrification of heating processes is a promising option. In order to investigate this transformation in energy system models, high-resolution temporal demand profiles of the heat and electricity applications for different industries are required. This paper presents a method for generating synthetic electricity and heat load profiles for 14 industry types. Using this methodology, annual profiles with a 15-minute resolution can be generated for both energy demands. First, daily profiles for the electricity demand were generated for 4 different production days. These daily profiles are additionally subdivided into eight end-use application categories. Finally, white noise is applied to the profile of the mechanical drives. The heat profile is similar to the electrical but is subdivided into four temperature ranges and the two applications hot water and space heating. The space heating application is additionally adjusted to the average monthly outdoor temperature. Both time series were generated for the analysis of an electrification of industrial heat application in energy system modelling.
Effect of downhill running on biomechanical risk factors associated with iliotibial band syndrome
(2022)
The purpose of this study was to identify the influence of downhill running on biomechanical risk factors for iliotibial band syndrome. We conducted a 3D motion analysis of 22 females and males running on an instrumented treadmill at four different inclinations (0%, -5%, -10%, -15%) at a speed of 3.5 m/s. We found significant differences for biomechanical risk factors associated with iliotibial band syndrome. Peak knee flexion angle at initial ground contact (p < .001), peak knee adduction angle (p = .005), and iliotibial band strain (p < .001) systematically increased with increasing slope. Downhill running increases biomechanical risk factors for iliotibial band syndrome. Our results highlight the need to consider the individual running environment in assessing overuse injury risk in runners.
The German Weather Service (DWD) releases a heat warning, when the weather forecast provides a warm, humid, sunny, and windless weather condition during the next days. The heat stress is calculated by the so called Klima-Michel model. If the apparent air temperature exceeds ca. 32°C / 38°C, there is a strong / extreme heat stress. The smallest forecast area is each administrative district. As people (and especially the vulnerable population) stay most of the time indoors, the heat health warning system was extended by the prediction of heat stress in typical rooms. Therewith it is feasible to forecast the heat stress using a combination of the outdoor and indoor heat stress. The prediction for the indoor heat stress is based on the same weather forecast like the Heat Health Warning Systems (HHWS).and calculates the heat stress by the PMV-model (predicted mean vote). Based on a sophisticated data analysis and simulation study, realistic but summer-critical living situations were defined and implemented in the building simulation program ESP-r. As the simulation runs especially for extreme weather conditions, a simplified building model cannot be used. Standardized input/output routines and an adaptive handover of start values provide for short run times for each forecast area. Good building designs and urban planning provide effective measures to reduce heat stress in cities. However, we have to also pay attention to the present building stock under climate change and a higher heat-wave risk. The extended German HHWS provide information for the emergency services to support the social assistants during heat waves.
This study presents some results from a monitoring project with night ventilation and earthto-air heat exchanger. Both techniques refer to air-based low-energy cooling. As these technologies are limited to specific boundary conditions (e.g. moderate summer climate, low temperatures during night, or low ground temperatures, respectively), water-based low-energy cooling may be preferred in many projects. A comparison of the night-ventilated building with a ground-cooled building shows major differences in both concepts.
Simulation-based degradation assessment of lithium-ion batteries in a hybrid electric vehicle
(2017)
The insufficient lifetime of lithium-ion batteries is one of the major cost driver for mobile applications. The battery pack in vehicles is one of the most expensive single components that practically must be excluded from premature replacement (i.e., before the life span of the other components end). Battery degradation is a complex physicochemical process that strongly depends on operating condition and environment. We present a simulation-based analysis of lithium-ion battery degradation during operation with a standard PHEV test cycle. We use detailed multiphysics (extended Newman-type) cell models that allow the assessment of local electrochemical potential, species and temperature distributions as driving forces for degradation, including solid electrolyte interphase (SEI) formation [1]. Fig. 1 shows an exemplary test cycle and the predicted resulting spatially-averaged SEI formation rate. We apply a time-upscaling approach to extrapolate the degradation analysis over long time scales, keeping physical accuracy while allowing end-of-life assessment [2]. Results are presented for lithium-ion battery cells with graphite/LFP chemistry. The behavior of these cells in terms of degradation propensity, performance, state of charge and other internal states is predicted during long-term cycling. State of health (SOH) is quantified as capacity fade and internal resistance increase as function of operation time.
Practical bottlenecks associated with commercialization of Lithium-air cells include capacity limitation and low cycling efficiency. The origin of such losses can be traced to complex electrochemical side reactions and reactant mass transport losses[1]. The efforts to minimize such losses include exploration of various electrolytes with additives[2], and cell component geometry and material design. Given the wide range of options for such materials, it is almost impractical to experimentally setup and characterize all those cells. Consequently, modeling and simulation studies are efficient alternatives to analyze spatially and temporally resolved cell behavior for various combinations of materials[3]. In this study, with the help of a two-dimensional multi physics model, we have focused on the effect of electrode and electrolyte interaction (electrochemistry), choice of electrolyte (species transport), and electrode geometry (electrode design) on the performance of a lithium-air button cell. Figure1a shows the schematics of the 2D axisymmetric computational domain. A comparative analysis of five different electrolytes was performed while focusing on the 2D distribution of local current density and the concentration of electro-chemically active species in the cell, that is, O2and Li+. Using two different cathode configurations, namely, flooded electrode and gas diffusion electrode (GDE)[4] at different cathode thickness, the effect of cell geometry and electrolyte saturation on cell performance was explored. Further, a detailed discussion on electrode volume utilization (cf. Figure1b) is presented via changes in the active volume of cathode that produces 90% of the total current with the cell current density for different combinations of electrolyte saturations and cathode thickness.
This study aimed to compare a simplified calculation of the knee abduction moment with the traditional inverse dynamics calculation when athletes perform fake-cut maneuvers with different complexities. In the simplified calculation, we multiply the force vector with its lever arm to the knee, projected onto the local coordinate system of the proximal thigh, hence neglecting the inertial contributions from distal segments. We found very strong ranking consistency using Spearman’s rank correlation coefficient when using the simplified method compared to the traditional calculation. Independent of the tasks, the simplified method resulted in higher moments than the inverse dynamics. This was caused by ignoring the moment caused by segment linear acceleration generating a counteracting moment by about 7%. An alternative to the complex calculations of inverse dynamics can be used to investigate the contributions of the GRF magnitude and its lever arm to the knee.
The increasing diffusion of rapidly developing AI technologies led to the idea of the experiment to combine TRIZ-based automated idea generation with the natural language processing tool ChatGPT, using the chatbot to interpret the automatically generated elementary solution principles. The article explores the opportunities and benefits of a novel AI-enhanced approach to teaching systematic innovation, analyses the learning experience, identifies the factors that affect students' innovation and problem-solving performance, and highlights the main difficulties students face, especially in interdisciplinary problems.
Process engineering industries are now facing growing economic pressure and societies' demands to improve their production technologies and equipment, making them more efficient and environmentally friendly. However unexpected additional technical and ecological drawbacks may appear as negative side effects of the new environmentally-friendly technologies. Thus, in their efforts to intensify upstream and downstream processes, industrial companies require a systematic aid to avoid compromising of ecological impact. The paper conceptualises a comprehensive approach for eco-innovation and eco- design in process engineering. The approach combines the advantages of Process Intensification as Knowledge-Based Engineering (KBE), inventive tools of Knowledge-Based Innovation (KBI), and main principles and best-practices of Eco-Design and Sustainable Manufacturing. It includes a correlation matrix for identification of eco-engineering contradictions and a process mapping technique for problem definition, database of Process Intensification methods and equipment, as well as a set of strongest inventive operators for eco-ideation.
Sustainable design of equipment for process intensification requires a comprehensive and correct identification of relevant stakeholder requirements, design problems and tasks crucial for innovation success. Combining the principles of the Quality Function Deployment with the Importance-Satisfaction Analysis and Contradiction Analysis of requirements gives an opportunity to define a proper process innovation strategy more reliably and to develop an optimal process intensification technology with less secondary engineering and ecological problems.
Economic growth and ecological problems have pushed industries to switch to eco-friendly technologies. However, environmental impact is still often neglected since production efficiency remains the main concern. Patent analysis in the field of process engineering shows that, on the one hand, some eco-issues appear as secondary problems of the new technologies, and on the other hand, eco-friendly solutions often show lower efficiency or performance capability. The study categorizes typical environmental problems and eco-contradictions in the field of process engineering involving solids handling and identifies underlying inventive principles that have a higher value for environmental innovation. Finally, 42 eco-innovation methods adapting TRIZ are chronologically presented and discussed.
As engineering graduates and specialists frequently lack the advanced skills and knowledge required to run eco-innovation systematically, the paper proposes a new teaching method and appropriate learning materials in the field of eco-innovation and evaluates the learning experience and outcomes. This programme is aimed at strengthening student’s skills and motivation to identify and creatively overcome secondary eco-contradictions in case if additional environmental problems appears as negative side effects of eco-friendly solutions.
Based on a literature analysis and own investigations, authors propose to introduce a manageable number of eco-innovation tools into a standard one-semester design course in process engineering with particular focus on the identification of eco-problems in existing technologies, selection of the appropriate new process intensification technologies (knowledge-based engineering), and systematic ideation and problem solving (knowledge-based innovation and invention).
The proposed educational approach equips students with the advanced knowledge, skills and competences in the field of eco-innovation. Analysis of the student’s work allows one to recommend simple-to-use tools for a fast application in process engineering, such as process mapping, database of eco-friendly process intensification technologies, and up to 20 strongest inventive operators for solving of environmental problems. For the majority of students in the survey, even the small workload has strengthened their self-confidence and skills in eco-innovation
Enhancing engineering creativity with automated formulation of elementary solution principles
(2023)
The paper describes a method for the automated formulation of elementary creative stimuli for product or process design at different levels of abstraction and in different engineering domains. The experimental study evaluates the impact of structured automated idea generation on inventive thinking in engineering design and compares it with previous experimental studies in educational and industrial settings. The outlook highlights the benefits of using automated ideation in the context of AI-assisted invention and innovation.
The paper is addressing the needs of the universities regarding qualification of students as future R&D specialists in efficient techniques for successfully running innovation process. It briefly describes the program of a novel one-semester-course of 150 hours in new product development and inventive problem solving with TRIZ methodology, offered for the master students at the Beuth University of Applied Sciences in Berlin. The paper outlines multi-source educational approach, which includes a new product development project (about 50% of the complete course), theory, practical work, self-learning with the software tools for computer-aided innovation, and demonstrates examples of the students work. The research part analyses the learning experience, identifies the factors that impact the innovation and problem solving performance of the students, and underlines the main difficulties faced by the students in the course. It describes a method for measurement of education efficiency and compares the results with educational experience in the industry. The presented results can help universities to establish the education in new product development or to improve its performance.
CONTEXT
The paper addresses the needs of medium and small businesses regarding qualification of R&D specialists in the interdisciplinary cross-industry innovation, which promises a considerable reduction of investments and R&D expenditures. The cross-industry innovation is commonly understood as identification of analogies and transfer of technologies, processes, technical solutions, working principles or business models between industrial sectors. However, engineering graduates and specialists frequently lack the advanced skills and knowledge required to run interdisciplinary innovation across the industry boundaries.
PURPOSE
The study compares the efficiency of the cross-industry innovation methods in one semester project-oriented course. It identifies the individual challenges and preferred working techniques of the students with different prior knowledge, sets of experiences, and cultural contexts, which require attention by engineering educators.
APPROACH
Two parallel one-semester courses were offered to the mechanical and process engineering students enrolled in bachelor’s and master’s degree programs at the faculty of mechanical and process engineering. The students from different years of study were working in 12 teams of 3…6 persons each on different innovation projects, spending two hours a week in the classroom and additionally on average two hours weekly on their project research. Students' feedback and self-assessments concerning gained skills, efficiency of learned tools and intermediate findings were documented, analysed, and discussed regularly along the course.
RESULTS
Analysis of numerous student projects allows to compare and to select the tools most appropriate for finding cross-industry solutions, such as thinking in analogies, web monitoring, function-oriented search, databases of technological effects and processes, special creativity techniques and others. The utilization of learned skills in practical innovation work strengthens the motivation of students and enhances their entrepreneurial competences. Suggested learning course and given recommendations help facilitate sustainable education of ambitious specialists.
CONCLUSIONS
The structured cross-industry innovation can be successfully run as a systematic process and learned in one semester course. The choice of the preferred working teqniques made by the students is affected by their prior knowledge in science, practical experience, and cultural contexts. Major outcomes of the students’ innovation projects such as feasibility, novelty and customer value of the concepts are primarily influenced by students’ engineering design skills, prior knowledge of the technologies, and industrial or business experience.
The comprehensive assessment method includes 80 innovation performance parameters and 10 key indicators of innovation capability, such as innovation process performance, innovating system performance, market and customer orientation, technology orientation, creativity, leadership, communication and knowledge management, risk and cost management, innovative climate, and innovation competences. The cross-industry study identifies parameters critical for innovation success and reveals different innovation performance patterns in companies.