Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Two-stage biohydrogen and methane production from sugarcane-based sugar and ethanol industrial wastes: A comprehensive review

The transition to renewable energy sources is crucial to ensure a sustainable future. Although the sugar and ethanol industries benefit from this transition, there are untapped opportunities to utilize the waste generated from the sugar and ethanol process chains through two-stage anaerobic digestion (TSAD). This review comprehensively discusses the utilization of various sugarcane-based industrial wastes by TSAD for sequential biohydrogen and methane production. Factors influencing TSAD process performance, including pH, temperature, hydraulic retention time, volatile fatty acids and alkalinity, nutrient imbalance, microbial population, and inhibitors, were discussed in detail. The potential of TSAD to reduce emissions of greenhouse gases is demonstrated. Recent findings, implications, and promising future research related to TSAD, including the integration of meta-omics approaches, gene manipulation and bioaugmentation, and application of artificial intelligence, are highlighted. The review can serve as important literature for the implementation, improvement, and advancements in TSAD research.

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

Ensuring the expected quality of materials is still a challenge, mainly in order to precisely plan improvement actions that allow for stabilization of the production process. Therefore, the purpose of this research was to develop a novel procedure to identify critical causes of material incompatibility-the causes that have the largest negative impact on material deterioration, and the natural environment. The main originality of this procedure is developing a way to coherent analyse the mutual influence of the many causes of incompatibility of any material, after which the critical causes are identified and a ranking of improvement actions to eliminate these causes is created. A novelty is also developed in the algorithm supporting this procedure, which can be realized in three different ways to solve this problem, i.e.; by considering the impact of material incompatibility on: (i) the deterioration of the material quality; (ii) the deterioration of the natural environment; and (iii) simultaneously the deterioration of the quality of the material and the natural environment. The effectiveness of this procedure was confirmed after tests on 410 alloy, from which a mechanical seal was made. However, this procedure can be useful for any material or industrial product.

Advances in the Monitoring, Diagnosis and Optimisation of Water Systems

In the context of global climate change, with the increasing frequency and severity of extreme events-such as draughts and floods-which will likely make water demand more uncertain and jeopardise its availability, those in charge of water system management face new operational challenges because of increasing resource scarcity, intensive energy requirements, growing populations (especially in urban areas), costly and ageing infrastructures, increasingly stringent regulations, and rising attention towards the environmental impact of water use [...].

Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important operational parameters, such as co-substrates ratio, their composition, volatile fatty acids/alkalinity ratio, organic loading rate, and solids/hydraulic retention time. AcoD process optimization, prediction and control, and early detection of system instability are often difficult to achieve through tedious manual monitoring processes. Recently, artificial intelligence (AI) has emerged as an innovative approach to computational modeling and optimization of the AcoD process. This review discusses AI applications in AcoD process optimization, control, prediction of unknown input/output parameters, and real-time monitoring. Furthermore, the review also compares standalone and hybrid AI algorithms as applied to AcoD. The review highlights future research directions for data preprocessing, model interpretation and validation, and grey-box modeling in AcoD process.

Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.