Techno‐Economic Assessment of a Microalgae Biorefinery

A preliminary techno-economic assessment of a microalgae biorefinery plant is reported, with pulsed electric field treatment (PEF) hydrothermal liquefaction as core technology. The results indicate that standalone production of microalgae biofuel would lead to an annual loss of 2.615 M€. PEF treatment could improve this scenario by bringing the microalgae biofuel to a competitive level (0.78 € kg$^{-1}$). Assuming that microalgae biofuel would be sold at the price of crude oil (0.44 € kg$^{-1}$), the minimum price of the amino-acid based product should be 7.56 € L$^{-1}$ for positive capital returns

Dynamic selection of clarification channels in rumor propagation containment

Rumors refer to spontaneously formed false stories. As rumors have shown severe threats to human society, it is significant to curb rumor propagation. Rumor clarification is an effective countermeasure on controlling rumor propagation. In this process, anti-rumor messages can be published through multiple media channels, including but not limited to online social platforms, TV programs and offline face-to-face campaigns. As the efficiency and cost of releasing anti-rumor information can vary from media channel to media channel, provided that the total budget is limited and fixed, it is valuable to investigate how to periodically select a combination of media channels to publish anti-rumor information so as to maximize the efficiency (i.e., make as many individuals as possible know the anti-rumor information) with the lowest cost. We refer to this issue as the dynamic channel selection (DCS) problem and any solution as a DCS strategy. To address the DCS problem, our contributions are as follows. First, we propose a rumor propagation model to characterize the influences of DCS strategies on curbing rumors. On this basis, we establish a trade-off model to evaluate DCS strategies and reduce the DCS problem to a mathematical optimization model called the DCS model. Second, based on the genetic algorithm framework, we develop a numerical method called the DCS algorithm to solve the DCS model. Third, we perform a series of numerical experiments to verify the performance of the DCS algorithm. Results show that the DCS algorithm can efficiently yield a satisfactory DCS strategy

Screening and Optimization of Microalgae Biomass and Plastic Material Coprocessing by Hydrothermal Liquefaction

In the past decade, microalgae biomass has been attracting considerable interest in valuable biocomponents and biofuel production. Meanwhile, plastic waste handling has become one of the most pressing global environmental concerns. Coprocessing of plastic waste and biomass has previously been reported to produce good quality fuel oil and high-value chemicals. In this study, we examined a coliquefaction process (co-HTL) of 2 microalgae, Chlorella vulgaris (Cv) and Nannochloropsis gaditana (Ng), with nine types of common plastics. In a first step, the co-HTL process was conducted in microautoclave reactors with a fixed algae/plastic mass ratio (50:50) at a temperature of 350 °C and a pressure of 16 MPa for a holding time of 15 min. Among the different types of plastics, positive synergistic effects between polycarbonate (PC), polystyrene (PS), and microalgae have been observed: (1) Plastics showed greater decomposition. (2) HTL crude oil yields were increased. Ng algae exhibits a higher interaction ability with plastics. Then, PC and PS were coprocessed with Ng algae using the response surface methodology to optimize the effects of temperature (300–400 °C), algae/plastic mass ratio (20:80–80:20), and holding time (5–45 min) on HTL crude oil yield. Software-based data analysis of the co-HTL experiments were conducted, and the optimal parameters were proposed, which were verified by the experiment results; Ng+PC (20:80 wt %) exhibits the highest crude oil yield of 67.2% at 300 °C with a 5 min holding time, while Ng+PS (80:20 wt %) generates 51.4 wt % crude oil yield at 400 °C and a 25 min holding time. Finally, the analytical results of elemental analysis, FTIR, 1H NMR, GPC, GC-MS, and TGA on the crude oil produced from pure microalgae HTL and co-HTL were compared, indicating that Ng+PC crude oil is more suitable for aromatic chemicals production and Ng+PS crude oil could be more favorable for biofuel applications

Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection

Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective

The Effect of Dichloromethane on Product Separation during Continuous Hydrothermal Liquefaction of Chlorella vulgaris and Aqueous Product Recycling for Algae Cultivation

Dichloromethane (DCM) is a solvent commonly used in laboratories for microalgae hydrothermal liquefaction (HTL) product separation. The addition of DCM would lead to an “overestimation effect” of biocrude yield and diminish biocrude quality. However, it is currently not clear to what extent this overestimation effect will impact a continuous HTL process. In this study, Chlorella vulgaris microalgae was processed in a continuous stirred tank reactor at different temperatures (300, 325, 350, 375, and 400 °C) at 24 MPa for 15 min holding time. Two separation methods were applied to investigate the effect of using DCM in a cHTL product separation procedure in terms of product yield, biocrude elemental content, and aqueous product (AP) composition. Subsequently, the feasibility of reusing AP for algae cultivation has been evaluated. Results suggest that 350 °C is the optimal temperature for cHTL operation, leading to the highest biocrude yield, and an average increase in biocrude yield of 9 wt % was achieved when using DCM in cHTL product separation. Within the temperature range investigated, an average biocrude yield estimation can be proposed by yield$_{non-DCM}$ ≈ 0.818 × yield$_{DCM}$. The AP has been characterized by total organic carbon and total nitrogen, high-performance liquid chromatography, and inductively coupled plasma optical emission spectroscopy. Results show that at 350–375 °C more nitrogen and other ions were directed into the AP, which could be advantageous in nutrient recovery. With the help of optical density testing, algae was shown to exhibit a better growth using AP with activated carbon absorption purification treatment as compared to the standard medium. The recovery of water and nutrients from the HTL-AP could improve the economics of a microalgae biorefinery process

Methylcap-Seq Reveals Novel DNA Methylation Markers for the Diagnosis and Recurrence Prediction of Bladder Cancer in a Chinese Population

PURPOSE: There is a need to supplement or supplant the conventional diagnostic tools, namely, cystoscopy and B-type ultrasound, for bladder cancer (BC). We aimed to identify novel DNA methylation markers for BC through genome-wide profiling of BC cell lines and subsequent methylation-specific PCR (MSP) screening of clinical urine samples. EXPERIMENTAL DESIGN: The methyl-DNA binding domain (MBD) capture technique, methylCap/seq, was performed to screen for specific hypermethylated CpG islands in two BC cell lines (5637 and T24). The top one hundred hypermethylated targets were sequentially screened by MSP in urine samples to gradually narrow the target number and optimize the composition of the diagnostic panel. The diagnostic performance of the obtained panel was evaluated in different clinical scenarios. RESULTS: A total of 1,627 hypermethylated promoter targets in the BC cell lines was identified by Illumina sequencing. The top 104 hypermethylated targets were reduced to eight genes (VAX1, KCNV1, ECEL1, TMEM26, TAL1, PROX1, SLC6A20, and LMX1A) after the urine DNA screening in a small sample size of 8 normal control and 18 BC subjects. Validation in an independent sample of 212 BC patients enabled the optimization of five methylation targets, including VAX1, KCNV1, TAL1, PPOX1, and CFTR, which was obtained in our previous study, for BC diagnosis with a sensitivity and specificity of 88.68% and 87.25%, respectively. In addition, the methylation of VAX1 and LMX1A was found to be associated with BC recurrence. CONCLUSIONS: We identified a promising diagnostic marker panel for early non-invasive detection and subsequent BC surveillance