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This paper examines a permutation flow-shop scheduling problem, which is a complex combinatorial problem encountered in many practical applications. The objective of the research is to reduce the maximum completion time, i.e., the makespan of all jobs. In order to increase productivity and to meet the demand, manufacturers are continuously under pressure to attain the shortest possible completion time. Estimation of accurate cycle time can tremendously help production planning and scheduling in manufacturing industries. Since production planning is characterised by NP-hardness and a wide range, traditional optimization methods and heuristic rules are unable to find satisfactory solutions. Q-learning, a type of reinforcement learning algorithm, is used in this paper to find a solution that is close to being optimal. Q-learning is a branch of machine learning referring to the way an intelligent agent should act in order to maximize the concept of cumulative reward in a given environment. To validate the performance of the algorithm, Taillard’s benchmark problems were solved and compared with the upper bound value. The results showed that the performance of the algorithm is better and has low computational time. Based on the performance of the proposed algorithm, two case studies were done and the solutions are compared with the performance of a metaheuristic algorithm. The result shows that the proposed algorithm can effectively and efficiently solve the problem stated above and that it is an interesting solution to resolving complex scheduling problems.

Compressed air without any traces of moisture is utmost important in several engineering applications. Heat regeneration-based adsorption dryer is mostly preferred to treat compressed air to ultra-low dew point. In this work, the essential characteristics of the different types heat regeneration mode adsorption dryer namely internal heating mode, external heating mode and compressed air heating mode are experimentally studied. The principal calculation schemes of adsorption dryers are based on the previous work and the calculations form the basis of a logical comparison between systems. The system assessment is carried out on common performance range for all modes namely compressed air output of 1000 to 5000 m3/hr, operating pressure of 5 to 10 bar, and feed air temperature of 25 to 45°C. The results revealed that the characteristic specific energy requirement in terms of electrical energy for internal heating regeneration, external heating regeneration and compressor air heating regeneration are 0.176 kW/m3/min,0.342 kW/m3/min, and 0 kW/m3/min respectively. In addition to that the characteristic specific energy requirement in terms of dried regeneration air requirement for internal heating, external heating and compressor air heating are 0.203 kW/m3/min,0 kW/m3/min, and 0 kW/m3/min respectively. It is also concluded that the energy and capital cost referred to 35°C feed air temperature and 8 bar abs. feed air pressure for 3000 m3/hr volume flow rate of product air attracts same amount for all the three modes of heat regeneration. Thus, a system comparison with reproducible dryer data for the varying performance ranges can be developed and utilized for practical purposes.

Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries. This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point (IBP) and end boiling point (EBP) in crude distillation unit. In this work, adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning (JITL) approach. The different types of local models designed are locally weighted regression (LWR), multiple linear regression (MLR), partial least squares regression (PLS) and support vector regression (SVR). In addition to model development, the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated. Results show that the JITL model based on support vector regression with iterative single data algorithm optimization (ISDA) local model (JITL-SVR:ISDA) yielded best prediction accuracy in reasonable computation time.

The majority of the energy expended by watercraft is used to overcome drag. Frictional resistance can account for up to 80 % of overall resistance, especially on large vessels, which leads to an increase the fuel consumption and environmental impacts. As a result, finding strategies to reduce frictional drag is interesting. There are numerous strategies for reducing drag that have already been proposed that are air lubrication techniques, boundary layer energizing, and suction is few of the examples. This study proposes a new way of reducing drag this method is known as water lubrication. To create adverse flow, water jets are used to split the flow. The formation of an auspicious boundary layer ore sluggish boundary layer surrounding the surface it reduces drag significantly. Separation is generally undesired since it expands the wake zone and as a result it produces drags. In flat plate separation it does not significantly enlarge the wake region. Separations form a slow or adversely directed layer on the flat plate if they are created and positioned correctly. This is demonstrated in this study through the use of ANSYS Fluent simulation. Also, with water jet velocities of 3 to 15 m/s drag reductions of 17 % to 134 % have been achieved.

Targeting protein-protein interactions for therapeutic development involves designing small molecules to either disrupt or enhance a known PPI. For this purpose, it is necessary to compute reliably the effect of chemical modifications of small molecules on the protein-protein association free energy. Here we present results obtained using a novel thermodynamic free energy cycle, for the rational design of allosteric inhibitors of HIV-1 integrase (ALLINI) that act specifically in the early stage of the infection cycle. The new compounds can serve as molecular probes to dissect the multifunctional mechanisms of ALLINIs to inform the discovery of new generations of the inhibitors. The free energy protocol developed here can be more broadly applied to study quantitatively the effects of small molecules on modulating the strengths of protein-protein interactions. This article is protected by copyright. All rights reserved.

Supplemental material, sj-docx-1-pie-10.1177_09544089221095663 for Impact of stir-squeeze casting process parameters on tensile strength of hybrid aluminium matrix composite by T. Pugalenthi, M. Arulraj, M. Sowrirajan and S. Vijayan in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering