Psychrophilic hydrogen production from petrochemical wastewater via anaerobic sequencing batch reactor: techno-economic assessment and kinetic modelling

Independent hydrogen production from petrochemical wastewater containing mono-ethylene glycol (MEG) via anaerobic sequencing batch reactor (ASBR) was extensively assessed under psychrophilic conditions (15–25 °C). A lab-scale ASBR was operated at pH of 5.50, and different organic loading rates (OLR) of 1.00, 1.67, 2.67, and 4.00 gCOD/L/d. The hydrogen yield (HY) progressed from 134.32 ± 10.79 to 189.09 ± 22.35 mL/gMEG_initial at increasing OLR from 1.00 to 4.00 gCOD/L/d. The maximum hydrogen content of 47.44 ± 3.60% was achieved at OLR of 4.0 gCOD/L/d, while methane content remained low (17.76 ± 1.27% at OLR of 1.0 gCOD/L/d). Kinetic studies using four different mathematical models were conducted to describe the ASBR performance. Furthermore, two batch-mode experiments were performed to optimize the nitrogen supplementation as a nutrient (C/N ratio), and assess the impact of salinity (as gNaCl/L) on hydrogen production. HY substantially dropped from 62.77 ± 4.09 to 6.02 ± 0.39 mL/gMEG_initial when C/N ratio was increased from 28.5 to 114.0. Besides, the results revealed that salinity up to 10.0 gNaCl/L has a relatively low inhibitory impact on hydrogen production. Eventually, the cost/benefit analysis showed that environmental and energy recovery revenues from ASBR were optimized at OLR of 4.0 gCOD/L/d (payback period of 7.13 yrs).     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.     

Insights into biological delignification of rice straw by Trametes hirsuta and Myrothecium roridum and comparison of saccharification yields with dilute acid pretreatment

Rice straw is the most abundant agricultural residue on a global scale and is widely available as feedstock for cellulosic fuel production. However, it is highly recalcitrant to biochemical deconstruction and also generates inhibitors that affect enzymatic saccharification. Rice straw from eastern Arkansas was subjected to dilute acid pretreatment (160 °C, 48 min and 1.0% sulfuric acid) and solid-state fermentation with two lignocellulolytic fungi, Trametes hirsuta and Myrothecium roridum, and their saccharification efficacies were compared. T. hirsuta and M. roridum were tested separately; pretreatment of rice straw with either strain for seven days resulted in 19 and 70% enrichment of its holocellulose content, respectively. However, liquid chromatography analysis of the alkali extracts showed significant differences in cell wall degradation by T. hirsuta and M. roridum. T. hirsuta removed 15% more phenolic compounds and 38% more glucan than M. roridum, while M. roridum removed 77% more xylan than T. hirsuta. Fungal and dilute acid pretreated biomass was then hydrolyzed using Accellerase^® 1500, a saccharification cocktail. Saccharification efficiency of M. roridum was 37% higher than that of dilute acid pretreatment of rice straw, requiring 8% lower enzyme loading and 50% shorter enzymatic hydrolysis duration. Alkali extraction of fungal pretreated biomass also yielded 10–15 g kg⁻¹ of acid precipitable polymeric lignin (APPL), which is a valuable co-product for biorefineries. In comparison to dilute acid pretreatment, fungal pretreatment could be a cost-effective alternative for the degradation of recalcitrant biomass, such as rice straw.     

Comparative evaluation of the mesophilic and thermophilic biohydrogen production at optimized conditions using tequila vinasses as substrate

The objective of this work was to comparatively evaluate the production of biohydrogen (bio-H₂) from tequila vinasses at optimized mesophilic and thermophilic conditions and to elucidate the main metabolic routes involved. Optimal temperatures of 35 °C and 55 °C, and pH of 5.5 maximized the bio-H₂ production rates, 25.5 ± 0.01 NmL h⁻¹ and 169.9 ± 8.9 NmL h⁻¹ in the mesophilic and thermophilic regimens, respectively. During the operation of anaerobic sequencing batch reactors, the thermophilic process allowed a volumetric bio-H₂ production rate of 519 ± 13 NmL-H₂ L⁻¹ d⁻¹ equivalent to 750 ± 19 NmL-H₂ L_vinasse⁻¹, while the mesophilic one 448 ± 23 NmL-H₂ L⁻¹ d⁻¹ and 647 ± 33 NmL-H₂ L_vinasse⁻¹, respectively. Furthermore, the gas produced under thermophilic conditions showed high hydrogen content (86.5%). Finally, formate degradation and glucose fermentation to acetic and butyric acids were the main metabolic routes involved in bio-H₂ production under thermophilic conditions, while at mesophilic conditions, the lactate and formate degradation pathways governed.     

Alkali catalyzed liquefaction of corncob in supercritical ethanol–water

Corncob liquefaction in supercritical ethanol–water was performed with and without the addition of an alkali catalyst by direct addition or biomass impregnation in a 250-cm³ batch reactor. The effects of temperature, solvent and alkali addition on the biomass conversion level and oil yield were investigated to find the optimum condition. For non-catalytic liquefaction using a 1:1 (v/v) ethanol: water ratio, a maximum oil yield and conversion level of 49.0% and 93.4%, respectively, were obtained at 340 °C. For alkali catalytic liquefaction, the oil yield with KOH addition (57.5%) was higher than that from KOH-impregnated corncob liquefaction (43.3%). The oil from liquefaction with KOH addition had higher heating value (26.7–35.3 MJ kg⁻¹) than the corncob (19.1 MJ kg⁻¹). The dominant components of the obtained oil were found by GC/MS analysis to be aldehyde, ester, phenol derivatives and aromatic compounds.     

OPAH a model for optimal design of multipurpose small hydropower plants

The investment in small hydropower plants requires the analysis of hydrological, technical, operational, budgetary, economical and financial aspects. The analysis of each possible configuration demands the joint action of several technicians, consuming substantial time and money. During initial design of the project, simplified procedures are usually adopted which may compromise the quality of the base configuration.In this paper we will present a global overview of the OPAH model, which was developed to surpass these limitations. This global model performs the optimization of project configuration. This model uses non-linear programming optimization to analyze the multipurpose operation of the hydropower plant. It uses a numeric simulation model of unsteady flow under pressure to analyze the hydraulic circuit. It uses an economical and financial simulation model that takes in to account the project risk associated to hydrologic and market variability, the financial capacity of the investor and the fiscal aspects.     

Solid–liquid phase equilibria of binary indole mixtures with some aromatic compounds using a solid–liquid–vapor equation-of-state

The separation of useful chemicals, such as indole from coal tar, is an important subject of research in the field of fossil energy, renewable energy, and utilization of byproducts and waste materials. High-pressure crystallization is one of the proposed separation methods. To understand the process requires detailed knowledge of solid–liquid phase behaviors. Solid–liquid phase equilibrium data of various binary mixtures of indole have been reported in the literature. In the present report, we analyze some of the experimental data with our unified solid–liquid–vapor equation-of-state to see whether our model can be useful for the calculation of solid–liquid equilibria in these organic systems. Good correlations of the data and predictions of phase behavior, at very high pressures, are demonstrated.