Technical and economic performance of the dithionite-assisted organosolv fractionation of lignocellulosic biomass

The development of biomass pretreatment approaches that, next to (hemi)cellulose valorization, aim at the conversion of lignin to chemicals is essential for the long-term success of a biorefinery. Herein, we discuss a dithionite-assisted organosolv fractionation (DAOF) of lignocellulose in n-butanol and water to produce cellulosic pulp and mono-/oligo-aromatics. The present study frames the technicalities of this biorefinery process and relates them to the features of the obtained product streams. Via the extensive characterization of the solid pulp (by acid hydrolysis-HPLC, ATR-FTIR, XRD, SEM and enzymatic hydrolysis-HPLC), of lignin derivatives (by GPC, GC-MS/FID, 1H-13C HSQC NMR, and ICP-AES) and of carbohydrate derivatives (by HPLC) we comprehensively identify and quantify the different products of interest. These results were used for inspecting the economic feasibility of DAOF. The adoption of a dithionite loading of 16.7% w/wbiomass and of an equivolumetric mixture of n-butanol and water, which led to a high yield of monophenolics (~20%, based on acid insoluble lignin, for the treatment of birch sawdust), was identified as the most profitable process configuration. Furthermore, the treatment of various lignocellulosic feedstocks was explored, which showed that DAOF is particularly effective for processing hardwood and herbaceous biomass. Overall, this study provides a comprehensive view of the development of an effective dithionite-assisted organosolv fractionation method for the sustainable upgrading of lignocellulosic biomass.

Computer-Aided Process Intensification of Natural gas to Methanol Process

The recent revolution in shale gas has presented opportunities for distributed manufacturing of key commodity chemicals, such as methanol, from methane. However, the conventional methane-to-methanol process is energy intensive which negatively affects the profitability and sustainability. We report an intensified process configuration that is both economically attractive and environmentally sustainable. This flowsheet is systematically discovered using the building block-based representation and optimization methodology. The new process configuration utilizes membrane-assisted reactive separations and can have as much as 190% higher total annual profit compared to a conventional configuration. Additionally, it has 57% less CO2-equivalent greenhouse gas (GHG) emission. Such drastic improvement highlights the advantages of building block-based computer-aided process intensification method.

A Formal Model for Configurable Business Process with Optimal Cloud Resource Allocation

In today’s competitive business environments, organizations increasingly need to model and deploy flexible and cost effective business processes. In this context, configurable process models are used to offer flexibility by representing process variants in a generic manner. Hence, the behavior of similar variants is grouped in a single model holding configurable elements. Such elements are then customized and configured depending on specific needs. However, the decision to configure an element may be incorrect leading to critical behavioral errors. Recently, process configuration has been extended to include Cloud resources allocation, to meet the need of business scalability by allowing access to on-demand IT resources. In this work, we propose a formal model based on propositional satisfiability formula allowing to find correct elements configuration including resources allocation ones. In addition, we propose to select optimal con- figurations based on Cloud resources cost. This approach allows to provide the designers with correct and cost-effective configuration decisions.

Gaussian Process Surrogate Modeling Under Control Uncertainties for Yield Prediction of Carbon Nanotube Production Processes

A large-scale production of carbon nanotubes has been of great interest due to their practical needs, which is limited by the difficulty of producing them with controlled structures and properties. We seek for a surrogate modeling to predict the process yield for a given process configuration under control uncertainties. The predictive power can be used to optimize the process configuration in a closed-loop production system. A challenge in the surrogate modeling is that some process conditions are controlled by other manipulating factors, and the control precision is not high. Therefore, the process conditions vary significantly even under the same setting of the manipulating factors. Due to this variation, the surrogate modeling that directly relates the manipulating factors to the process outcome does not provide a great predictive power on the outcome. At the same time, the model relating the process conditions to the outcome is not appropriate for the prediction purpose because the process conditions cannot be accurately set as planned due to the control uncertainties for a future process run. Motivated by the example, we propose a two-tiered Gaussian process (GP) model, where the bottom tier relates the manipulating factors to the process conditions with control variation, and the top tier relates the process conditions to the outcome. It explicitly models the propagation of the control uncertainty to the outcome through the two modeling tiers. The benefits of the approach over the standard GP approach are illustrated with multiple simulated scenarios and carbon nanotube production processes.

Dynamic Recrystallization and Recovery Behaviors in Austenite of a Novel Fe-1.93Mn-0.07Ni-1.96Cr-0.35Mo Ultrahigh Strength Steel

Due to the complex composition and high proportion of alloys in traditional ultrahigh strength steel, the dilemma caused by ultrahigh strength and low toughness in casting and forging processes requiring subsequent heat treatment can be mitigated with an efficient and economical rolling process. In this work, the effect of deformation parameters on dynamic recrystallization (DRX) and dynamic recovery (DRV) is discussed through stress-strain analysis, the DRV mathematical model is obtained, and then the dynamic recrystallization activation energy, Zener–Hollomon equation, and hot working equation are obtained. The critical strain of DRX detected by the P-J method is ε c / ε p = 0.631 , which indicates that dynamic recrystallization of this novel steel is relatively easy to achieve by the rolling process. These models and conclusions have potential to be generalized for the formulation of process specification and process configuration without requiring extensive material testing.

Scenario-Based Techno-Economic Analysis of Steam Methane Reforming Process for Hydrogen Production

Steam methane reforming (SMR) process is regarded as a viable option to satisfy the growing demand for hydrogen, mainly because of its capability for the mass production of hydrogen and the maturity of the technology. In this study, an economically optimal process configuration of SMR is proposed by investigating six scenarios with different design and operating conditions, including CO2 emission permits and CO2 capture and sale. Of the six scenarios, the process configuration involving CO2 capture and sale is the most economical, with an H2 production cost of $1.80/kg-H2. A wide range of economic analyses is performed to identify the tradeoffs and cost drivers of the SMR process in the economically optimal scenario. Depending on the CO2 selling price and the CO2 capture cost, the economic feasibility of the SMR-based H2 production process can be further improved.

Learning how to learn and then doing it all over again: The evolving learning modes of migrant entrepreneurs

Building on an in-depth study of 12 Bulgarian migrant entrepreneurial company cases in London, we illustrate how migrant entrepreneurs (MEs) interact with, and learn from, their exposure to a diaspora network. We demonstrate that learning processes need to be studied within the context where they occur as MEs adapt their modes of learning to contextual changes. We use social learning theory to offer a situated process model of learning, which shows why and how learning evolves over time, the learning modes MEs undergo (i.e. observational, participative, and exploratory learning), as well as the process configuration within which these learning modes are rooted. This article adds to the growing body of work showing the boundary conditions and the mechanisms through which MEs learn from networks when operating in a foreign market.